R/estimHI_3FDR.R
In floguillet/MCSPCD: Monte Carlo Simulation for Projections of epidemiological burden of Chronic Disease
Defines functions
estimHI_3FDR
Documented in
estimHI_3FDR
#' Computation of Health Indicators
#'
#' This function computes many health indicators under several scenarios of intervention in risk factor distribution for a given year.
#'
#' @param t year of the projections for health indicators.
#' @param intervention 0 = no change; 1 = risk factor prevalence and incidence considered as null. Default is \code{0}.
#' @param year_intervention year of the intervention in risk factor distribution takes place. Default is \code{NULL}.
#' @param nb_people number of people whose trajectory will be simulated for each generation. Default is \code{100}.
#' @param nb_iter number of iterations for the algorithm. Default is \code{0}.
#' @param data_pop data source for demographics data.
#' @param gender gender for computation. \code{"W"} for women and \code{"M"} for men. Default is \code{"W"}.
#' @param data_a01_values data source for the incidence of disease.
#' @param data_a02_values data source for the mortality of healthy subjects.
#' @param data_theta01_1_values data source for the relative risks associated with the exposure 1 for disease.
#' @param data_theta01_2_values data source for the relative risks associated with the exposure 2 for disease.
#' @param data_theta01_3_values data source for the relative risks associated with the exposure 3 for disease.
#' @param data_theta02_1_values data source for the relative risks associated with the exposure 1 for mortality among healthy subjects.
#' @param data_theta02_2_values data source for the relative risks associated with the exposure 2 for mortality among healthy subjects.
#' @param data_theta02_3_values data source for the relative risks associated with the exposure 3 for mortality among healthy subjects.
#' @param data_theta12_1_values data source for the relative risks associated with the exposure 1 for mortality among diseased subjects.
#' @param data_theta12_2_values data source for the relative risks associated with the exposure 2 for mortality among diseased subjects.
#' @param data_theta12_3_values data source for the relative risks associated with the exposure 3 for mortality among diseased subjects.
#' @param data_prev_0_values data source for the prevalence of the exposition 0.
#' @param data_prev_1_values data source for the prevalence of the exposition 1.
#' @param data_prev_2_values data source for the prevalence of the exposition 2.
#' @param data_prev_3_values data source for the prevalence of the exposition 3.
#' @param data_prev_4_values data source for the prevalence of the exposition 4.
#' @param data_prev_5_values data source for the prevalence of the exposition 5.
#' @param data_prev_6_values data source for the prevalence of the exposition 6.
#' @param data_prev_7_values data source for the prevalence of the exposition 7.
#' @param data_incid_0_values data source for the incidence of the exposition 0.
#' @param data_incid_1_values data source for the incidence of the exposition 1.
#' @param data_incid_3_values data source for the incidence of the exposition 3.
#' @param data_incid_5_values data source for the incidence of the exposition 5.
#' @param data_rr_DvsND_values data source for the relative risks associated with the disease for mortality.
#' @param data_a01 variability of data source for the incidence of disease.
#' @param data_a02 variability of data source for the mortality of healthy subjects.
#' @param data_theta01_1 variability of data source for the relative risks associated with the exposure 1 for disease.
#' @param data_theta01_2 variability of data source for the relative risks associated with the exposure 2 for disease.
#' @param data_theta01_3 variability of data source for the relative risks associated with the exposure 3 for disease.
#' @param data_theta02_1 variability of data source for the relative risks associated with the exposure 1 for mortality among healthy subjects.
#' @param data_theta02_2 variability of data source for the relative risks associated with the exposure 2 for mortality among healthy subjects.
#' @param data_theta02_3 variability of data source for the relative risks associated with the exposure 3 for mortality among healthy subjects.
#' @param data_theta12_1 variability of data source for the relative risks associated with the exposure 1 for mortality among diseased subjects.
#' @param data_theta12_2 variability of data source for the relative risks associated with the exposure 2 for mortality among diseased subjects.
#' @param data_theta12_3 variability of data source for the relative risks associated with the exposure 3 for mortality among diseased subjects.
#' @param data_prev_0 variability of data source for the prevalence of the exposition 0.
#' @param data_prev_1 variability of data source for the prevalence of the exposition 1.
#' @param data_prev_2 variability of data source for the prevalence of the exposition 2.
#' @param data_prev_3 variability of data source for the prevalence of the exposition 3.
#' @param data_prev_4 variability of data source for the prevalence of the exposition 4.
#' @param data_prev_5 variability of data source for the prevalence of the exposition 5.
#' @param data_prev_6 variability of data source for the prevalence of the exposition 6.
#' @param data_prev_7 variability of data source for the prevalence of the exposition 7.
#' @param data_incid_0 variability of data source for the incidence of the exposition 0.
#' @param data_incid_1 variability of data source for the incidence of the exposition 1.
#' @param data_incid_3 variability of data source for the incidence of the exposition 3.
#' @param data_incid_5 variability of data source for the incidence of the exposition 5.
#' @param data_rr_DvsND variability of data source for the relative risks associated with the disease for mortality.
#' @param Ncpus The number of processors available. Default is \code{"1"}.
#'
#' @return a list containing the health indicators
#'
#' @export
#'
#' @examples
#' estimHI_3FDR(t = 2040,
#' intervention = 1,
#' year_intervention = 2020,
#' nb_people = 10000,
#' nb_iter = 100,
#' data_pop = pop,
#' gender = "W",
#' data_a01_values = a01_constant_values,
#' data_a02_values = a02_constant_values,
#' data_theta01_1_values = theta01_cas_1_6_values,
#' data_theta01_2_values = theta01_cas_1_6_values,
#' data_theta01_3_values = theta01_cas_1_6_values,
#' data_theta02_1_values = theta02_increase_values,
#' data_theta02_2_values = theta02_increase_values,
#' data_theta02_3_values = theta02_increase_values,
#' data_theta12_1_values = theta02_increase_values,
#' data_theta12_2_values = theta02_increase_values,
#' data_theta12_3_values = theta02_increase_values,
#' data_prev_0_values <- prevconso_values,
#' data_prev_1_values <- prevconso_values,
#' data_prev_2_values <- prevconso_values,
#' data_prev_3_values <- prevconso_values,
#' data_prev_4_values <- prevconso_values,
#' data_prev_5_values <- prevconso_values,
#' data_prev_6_values <- prevconso_values,
#' data_prev_7_values <- prevconso_values,
#' data_incid_0_values <- incidconso_values,
#' data_incid_1_values <- incidconso_values,
#' data_incid_3_values <- incidconso_values,
#' data_incid_5_values <- incidconso_values,
#' data_rr_DvsND_values = rr_DvsND_values,
#' data_a01 = a01_constant,
#' data_a02 = a02_constant,
#' data_theta01_1 = theta01_cas_1_6,
#' data_theta01_2 = theta01_cas_1_6,
#' data_theta01_3 = theta01_cas_1_6,
#' data_theta02_1 = theta02_increase,
#' data_theta02_2 = theta02_increase,
#' data_theta02_3 = theta02_increase,
#' data_theta12_1 = theta02_increase,
#' data_theta12_2 = theta02_increase,
#' data_theta12_3 = theta02_increase,
#' data_prev_0 = prevconso,
#' data_prev_1 = prevconso,
#' data_prev_2 = prevconso,
#' data_prev_3 = prevconso,
#' data_prev_4 = prevconso,
#' data_prev_5 = prevconso,
#' data_prev_6 = prevconso,
#' data_prev_7 = prevconso,
#' data_incid_0 = incidconso,
#' data_incid_1 = incidconso,
#' data_incid_3 = incidconso,
#' data_incid_5 = incidconso,
#' data_rr_DvsND = rr_DvsND,
#' Ncpus = 1)
estimHI_3FDR <- function(t,
intervention = 0,
year_intervention = NULL,
nb_people = 100,
nb_iter = 0,
data_pop,
gender = "W",
data_a01_values,
data_a02_values,
data_theta01_1_values,
data_theta01_2_values,
data_theta01_3_values,
data_theta02_1_values,
data_theta02_2_values,
data_theta02_3_values,
data_theta12_1_values,
data_theta12_2_values,
data_theta12_3_values,
data_prev_0_values,
data_prev_1_values,
data_prev_2_values,
data_prev_3_values,
data_prev_4_values,
data_prev_5_values,
data_prev_6_values,
data_prev_7_values,
data_incid_0_values,
data_incid_1_values,
data_incid_3_values,
data_incid_5_values,
data_rr_DvsND_values,
data_a01,
data_a02,
data_theta01_1,
data_theta01_2,
data_theta01_3,
data_theta02_1,
data_theta02_2,
data_theta02_3,
data_theta12_1,
data_theta12_2,
data_theta12_3,
data_prev_0,
data_prev_1,
data_prev_2,
data_prev_3,
data_prev_4,
data_prev_5,
data_prev_6,
data_prev_7,
data_incid_0,
data_incid_1,
data_incid_3,
data_incid_5,
data_rr_DvsND,
Ncpus = 1)
{
### Year of projection
set.seed(0)
year_proj <- t
### Incidence of disease on non exposed peoples
a010 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a010) <- c("age",1950:2080)
a010[,1] <- c(66:105)
for (a in 2:ncol(a010)){
a010[,a] <- as.numeric(data_a01[which(data_a01[,1] != 65 & data_a01[,2]%in%(gender)),a+1]) / (data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2] + data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2] + data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2] + data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2]*data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2] + data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2] + data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2]*data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2] + data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2]*data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2] + data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2]*data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]);
}
### Incidence of disease on exposed peoples
a011 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a011) <- c("age",1950:2080)
a011[,1] <- c(66:105)
a011[,-1] <- a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2];
a012 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a012) <- c("age",1950:2080)
a012[,1] <- c(66:105)
a012[,-1] <- a010[,-1]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2];
a013 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a013) <- c("age",1950:2080)
a013[,1] <- c(66:105)
a013[,-1] <- a010[,-1]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2];
a014 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a014) <- c("age",1950:2080)
a014[,1] <- c(66:105)
a014[,-1] <- a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2];
a015 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a015) <- c("age",1950:2080)
a015[,1] <- c(66:105)
a015[,-1] <- a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2];
a016 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a016) <- c("age",1950:2080)
a016[,1] <- c(66:105)
a016[,-1] <- a010[,-1]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2];
a017 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a017) <- c("age",1950:2080)
a017[,1] <- c(66:105)
a017[,-1] <- a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2];
### Global incidence of disease
a01_global <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a01_global) <- c("age",1950:2080)
a01_global[,1] <- c(66:105)
a01_global[,-1] <- data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2]*a010[,-1] + data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2]*a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2] + data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2]*a010[,-1]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2] + data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2]*a010[,-1]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2] + data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2]*a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2] + data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2]*a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2] + data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2]*a010[,-1]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2] + data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]*a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2];
### Mortality of healthy subjects on non exposed peoples
a020 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a020) <- c("age",1950:2080)
a020[,1] <- c(66:105)
for (a in 2:ncol(a020)){
a020[,a] <- as.numeric(data_a02[which(data_a02[,1] != 65 & data_a02[,2]%in%(gender)),a+1]) / (data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2] + data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2] + data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2] + data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2]*data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2] + data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2] + data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2]*data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2] + data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2]*data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2] + data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2]*data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]);
}
### Mortality of healthy subjects on exposed peoples
a021 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a021) <- c("age",1950:2080)
a021[,1] <- c(66:105)
a021[,-1] <- a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2];
a022 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a022) <- c("age",1950:2080)
a022[,1] <- c(66:105)
a022[,-1] <- a020[,-1]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2];
a023 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a023) <- c("age",1950:2080)
a023[,1] <- c(66:105)
a023[,-1] <- a020[,-1]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2];
a024 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a024) <- c("age",1950:2080)
a024[,1] <- c(66:105)
a024[,-1] <- a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2];
a025 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a025) <- c("age",1950:2080)
a025[,1] <- c(66:105)
a025[,-1] <- a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2];
a026 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a026) <- c("age",1950:2080)
a026[,1] <- c(66:105)
a026[,-1] <- a020[,-1]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2];
a027 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a027) <- c("age",1950:2080)
a027[,1] <- c(66:105)
a027[,-1] <- a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2];
### Global mortality of healthy subjects
a02_global <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a02_global) <- c("age",1950:2080)
a02_global[,1] <- c(66:105)
a02_global[,-1] <- data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2]*a020[,-1] + data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2]*a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2] + data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2]*a020[,-1]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2] + data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2]*a020[,-1]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2] + data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2]*a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2] + data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2]*a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2] + data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2]*a020[,-1]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2] + data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]*a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2];
### Relative risks associated with the disease for mortality
RR <- matrix(c(0),
nrow=40*nb_iter,
ncol=2,
byrow=T);
colnames(RR) <- c("age","rr_DvsND")
RR[,1] <- c(66:105)
RR[,2] <- data_rr_DvsND[which(data_rr_DvsND[,1] != 65 & data_rr_DvsND[,3]%in%(gender)),2];
### Mortality of diseased subjects on non exposed peoples
a120 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a120) <- c("age",1950:2080)
a120[,1] <- c(66:105)
for (a in 2:ncol(a120)){
a120[,a] <- as.numeric(RR[,2])*as.numeric(data_a02[which(data_a02[,1] != 65 & data_a02[,2]%in%(gender)),a+1]) / (data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2] + data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2] + data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2] + data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2]*data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2] + data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2] + data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2]*data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2] + data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2]*data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2] + data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2]*data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]);
}
### Mortality of diseased subjects on exposed peoples
a121 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a121) <- c("age",1950:2080)
a121[,1] <- c(66:105)
a121[,-1] <- a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2];
a122 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a122) <- c("age",1950:2080)
a122[,1] <- c(66:105)
a122[,-1] <- a120[,-1]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2];
a123 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a123) <- c("age",1950:2080)
a123[,1] <- c(66:105)
a123[,-1] <- a120[,-1]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2];
a124 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a124) <- c("age",1950:2080)
a124[,1] <- c(66:105)
a124[,-1] <- a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2];
a125 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a125) <- c("age",1950:2080)
a125[,1] <- c(66:105)
a125[,-1] <- a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2];
a126 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a126) <- c("age",1950:2080)
a126[,1] <- c(66:105)
a126[,-1] <- a120[,-1]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2];
a127 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a127) <- c("age",1950:2080)
a127[,1] <- c(66:105)
a127[,-1] <- a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2];
### Global mortality of diseased subjects
a12_global <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a12_global) <- c("age",1950:2080)
a12_global[,1] <- c(66:105)
a12_global[,-1] <- data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2]*a120[,-1] + data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2]*a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2] + data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2]*a120[,-1]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2] + data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2]*a120[,-1]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2] + data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2]*a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2] + data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2]*a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2] + data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2]*a120[,-1]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2] + data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]*a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2];
### Variability of parameters
### Incidence of disease on non exposed peoples
a010_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a010_values) <- c("age",1950:2080)
a010_values[,1] <- c(66:105)
for (a in 2:ncol(a010_values)){
a010_values[,a] <- as.numeric(data_a01_values[which(data_a01_values[,1] != 65 & data_a01_values[,2]%in%(gender)),a+1]) / (data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2] + data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2] + data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2] + data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]);
}
### Incidence of disease on exposed peoples
a011_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a011_values) <- c("age",1950:2080)
a011_values[,1] <- c(66:105)
a011_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2];
a012_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a012_values) <- c("age",1950:2080)
a012_values[,1] <- c(66:105)
a012_values[,-1] <- a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2];
a013_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a013_values) <- c("age",1950:2080)
a013_values[,1] <- c(66:105)
a013_values[,-1] <- a010_values[,-1]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a014_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a014_values) <- c("age",1950:2080)
a014_values[,1] <- c(66:105)
a014_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2];
a015_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a015_values) <- c("age",1950:2080)
a015_values[,1] <- c(66:105)
a015_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a016_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a016_values) <- c("age",1950:2080)
a016_values[,1] <- c(66:105)
a016_values[,-1] <- a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a017_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a017_values) <- c("age",1950:2080)
a017_values[,1] <- c(66:105)
a017_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
### Global incidence of disease
a01_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a01_global_values) <- c("age",1950:2080)
a01_global_values[,1] <- c(66:105)
a01_global_values[,-1] <- data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2]*a010_values[,-1] + data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2] + data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2] + data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2] + data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
### Mortality of healthy subjects on non exposed peoples
a020_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a020_values) <- c("age",1950:2080)
a020_values[,1] <- c(66:105)
for (a in 2:ncol(a020_values)){
a020_values[,a] <- as.numeric(data_a02_values[which(data_a02_values[,1] != 65 & data_a02_values[,2]%in%(gender)),a+1]) / (data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2] + data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2] + data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2] + data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]);
}
### Mortality of healthy subjects on exposed peoples
a021_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a021_values) <- c("age",1950:2080)
a021_values[,1] <- c(66:105)
a021_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2];
a022_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a022_values) <- c("age",1950:2080)
a022_values[,1] <- c(66:105)
a022_values[,-1] <- a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2];
a023_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a023_values) <- c("age",1950:2080)
a023_values[,1] <- c(66:105)
a023_values[,-1] <- a020_values[,-1]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a024_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a024_values) <- c("age",1950:2080)
a024_values[,1] <- c(66:105)
a024_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2];
a025_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a025_values) <- c("age",1950:2080)
a025_values[,1] <- c(66:105)
a025_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a026_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a026_values) <- c("age",1950:2080)
a026_values[,1] <- c(66:105)
a026_values[,-1] <- a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a027_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a027_values) <- c("age",1950:2080)
a027_values[,1] <- c(66:105)
a027_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
### Global mortality of healthy subjects
a02_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a02_global_values) <- c("age",1950:2080)
a02_global_values[,1] <- c(66:105)
a02_global_values[,-1] <- data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2]*a020_values[,-1] + data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2] + data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2] + data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2] + data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
### Relative risks associated with the disease for mortality
RR_values <- matrix(c(0),
nrow=40,
ncol=2,
byrow=T);
colnames(RR_values) <- c("age","rr_DvsND")
RR_values[,1] <- c(66:105)
RR_values[,2] <- data_rr_DvsND_values[which(data_rr_DvsND_values[,1] != 65 & data_rr_DvsND_values[,3]%in%(gender)),2];
### Mortality of diseased subjects on non exposed peoples
a120_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a120_values) <- c("age",1950:2080)
a120_values[,1] <- c(66:105)
for (a in 2:ncol(a120_values)){
a120_values[,a] <- as.numeric(RR_values[,2])*as.numeric(data_a02_values[which(data_a02_values[,1] != 65 & data_a02_values[,2]%in%(gender)),a+1]) / (data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2] + data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2] + data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2] + data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]);
}
### Mortality of diseased subjects on exposed peoples
a121_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a121_values) <- c("age",1950:2080)
a121_values[,1] <- c(66:105)
a121_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2];
a122_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a122_values) <- c("age",1950:2080)
a122_values[,1] <- c(66:105)
a122_values[,-1] <- a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2];
a123_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a123_values) <- c("age",1950:2080)
a123_values[,1] <- c(66:105)
a123_values[,-1] <- a120_values[,-1]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a124_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a124_values) <- c("age",1950:2080)
a124_values[,1] <- c(66:105)
a124_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2];
a125_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a125_values) <- c("age",1950:2080)
a125_values[,1] <- c(66:105)
a125_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a126_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a126_values) <- c("age",1950:2080)
a126_values[,1] <- c(66:105)
a126_values[,-1] <- a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a127_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a127_values) <- c("age",1950:2080)
a127_values[,1] <- c(66:105)
a127_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
### Global mortality of diseased subjects
a12_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a12_global_values) <- c("age",1950:2080)
a12_global_values[,1] <- c(66:105)
a12_global_values[,-1] <- data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2]*a120_values[,-1] + data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2] + data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2] + data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2] + data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
### Matrix for results of health indicators
### Overall life-expectancy
esp_vie_gen <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_gen[,1] <- c(65:100);
### Overall life-expectancy on exposed peoples
esp_vie_gen_conso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_gen_conso[,1] <- c(65:100);
### Overall life-expectancy on non exposed peoples
esp_vie_gen_nonconso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_gen_nonconso[,1] <- c(65:100);
### Life-expectancy without disease
esp_vie_sans_mal <- matrix(c(0), # matrice de calcul des espérances de vie à chaque âge
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_sans_mal[,1] <- c(65:100);
### Life-expectancy without disease on exposed peoples
esp_vie_sans_mal_conso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_sans_mal_conso[,1] <- c(65:100);
### Life-expectancy without disease on non exposed peoples
esp_vie_sans_mal_nonconso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_sans_mal_nonconso[,1] <- c(65:100);
### Life-expectancy for diseased subject
esp_vie_mal <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_mal[,1] <- c(65:100);
### Life-expectancy for diseased subject on exposed peoples
esp_vie_mal_conso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_mal_conso[,1] <- c(65:100);
### Life-expectancy for diseased subject on non exposed peoples
esp_vie_mal_nonconso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_mal_nonconso[,1] <- c(65:100);
### Life-expectancy for non-diseased subject
esp_vie_non_mal <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_non_mal[,1] <- c(65:100);
### Life-expectancy for non-diseased subject on exposed peoples
esp_vie_non_mal_conso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_non_mal_conso[,1] <- c(65:100);
### Life-expectancy for non-diseased subject on non exposed peoples
esp_vie_non_mal_nonconso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_non_mal_nonconso[,1] <- c(65:100);
### Number of exposed peoples at least one time
prevalence <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
prevalence[,1] <- c(65:99);
### Rate of exposed peoples at least one time
taux_prevalence <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
taux_prevalence[,1] <- c(65:99);
### Number of living peoples
survie <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
survie[,1] <- c(65:99);
### Rate of living peoples a given
taux_survivants <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
taux_survivants[,1] <- c(65:99);
### Mean number of years spent with disease
nb_moy_dem <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
nb_moy_dem[,1] <- c(65:100);
### Mean number of years spent with disease on exposed peoples
nb_moy_dem_conso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
nb_moy_dem_conso[,1] <- c(65:100);
### Mean number of years spent with disease on non exposed peoples
nb_moy_dem_nonconso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
nb_moy_dem_nonconso[,1] <- c(65:100);
### Life-long probability of disease
prb_dem <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
prb_dem[,1] <- c(65:99);
### Average age at disease onset
age_dem <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
age_dem[,1] <- c(65:99);
### Mean number of years of exposition
moy_conso <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
moy_conso[,1] <- c(65:99);
### Number of exposed peoples at least one time
prevalence_conso <- matrix(c(0),
nrow=105-65+1,
ncol=2+nb_iter,
byrow=T);
prevalence_conso[,1] <- c(65:105);
### Mortality rate
quotient_mortalite <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
quotient_mortalite[,1] <- c(65:99);
##############################
########## ESSAIS ############
##############################
### Dementia incidence
incid_demence <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
incid_demence[,1] <- c(66:105);
### Mortality incidence
incid_mort_Dem <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
incid_mort_Dem[,1] <- c(66:105);
incid_mort_NoD <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
incid_mort_NoD[,1] <- c(66:105);
### Non exposed prevalence
prevalence_noe <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_noe[,1] <- c(65:105);
### Physical inactivity prevalence
prevalence_ina <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_ina[,1] <- c(65:105);
### Hypertension prevalence
prevalence_hta <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_hta[,1] <- c(65:105);
### Diabete prevalence
prevalence_dia <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_dia[,1] <- c(65:105);
### Expositions prevalence
prevalence_exp0 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp0[,1] <- c(65:105);
prevalence_exp1 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp1[,1] <- c(65:105);
prevalence_exp2 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp2[,1] <- c(65:105);
prevalence_exp3 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp3[,1] <- c(65:105);
prevalence_exp4 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp4[,1] <- c(65:105);
prevalence_exp5 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp5[,1] <- c(65:105);
prevalence_exp6 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp6[,1] <- c(65:105);
prevalence_exp7 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp7[,1] <- c(65:105);
prop_dem_diabet <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
prop_dem_diabet[,1] <- c(66:105);
prop_dem_hypert <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
prop_dem_hypert[,1] <- c(66:105);
prop_dem_inacti <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
prop_dem_inacti[,1] <- c(66:105);
prop_dem_global <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
prop_dem_global[,1] <- c(66:105);
#####################################################################################
#####################################################################################
#####################################################################################
#####################################################################################
#####################################################################################
### Computation with estimated parameters
for (age in 65:105) { # for each generation
an_naiss <- year_proj-age; # year of birth
an0 <- an_naiss + 65; # first year at risk
annee <- an0; # year of estimation
data_prev_0_values_ND <- data_prev_0_values
data_prev_0_values_D <- data_prev_0_values
data_prev_1_values_ND <- data_prev_1_values
data_prev_1_values_D <- data_prev_1_values
data_prev_2_values_ND <- data_prev_2_values
data_prev_2_values_D <- data_prev_2_values
data_prev_3_values_ND <- data_prev_3_values
data_prev_3_values_D <- data_prev_3_values
data_prev_4_values_ND <- data_prev_4_values
data_prev_4_values_D <- data_prev_4_values
data_prev_5_values_ND <- data_prev_5_values
data_prev_5_values_D <- data_prev_5_values
data_prev_6_values_ND <- data_prev_6_values
data_prev_6_values_D <- data_prev_6_values
data_prev_7_values_ND <- data_prev_7_values
data_prev_7_values_D <- data_prev_7_values
a010_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a010_values) <- c("age",1950:2080)
a010_values[,1] <- c(66:105)
a011_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a011_values) <- c("age",1950:2080)
a011_values[,1] <- c(66:105)
a012_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a012_values) <- c("age",1950:2080)
a012_values[,1] <- c(66:105)
a013_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a013_values) <- c("age",1950:2080)
a013_values[,1] <- c(66:105)
a014_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a014_values) <- c("age",1950:2080)
a014_values[,1] <- c(66:105)
a015_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a015_values) <- c("age",1950:2080)
a015_values[,1] <- c(66:105)
a016_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a016_values) <- c("age",1950:2080)
a016_values[,1] <- c(66:105)
a017_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a017_values) <- c("age",1950:2080)
a017_values[,1] <- c(66:105)
a01_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a01_global_values) <- c("age",1950:2080)
a01_global_values[,1] <- c(66:105)
a020_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a020_values) <- c("age",1950:2080)
a020_values[,1] <- c(66:105)
a021_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a021_values) <- c("age",1950:2080)
a021_values[,1] <- c(66:105)
a022_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a022_values) <- c("age",1950:2080)
a022_values[,1] <- c(66:105)
a023_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a023_values) <- c("age",1950:2080)
a023_values[,1] <- c(66:105)
a024_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a024_values) <- c("age",1950:2080)
a024_values[,1] <- c(66:105)
a025_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a025_values) <- c("age",1950:2080)
a025_values[,1] <- c(66:105)
a026_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a026_values) <- c("age",1950:2080)
a026_values[,1] <- c(66:105)
a027_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a027_values) <- c("age",1950:2080)
a027_values[,1] <- c(66:105)
a02_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a02_global_values) <- c("age",1950:2080)
a02_global_values[,1] <- c(66:105)
a120_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a120_values) <- c("age",1950:2080)
a120_values[,1] <- c(66:105)
a121_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a121_values) <- c("age",1950:2080)
a121_values[,1] <- c(66:105)
a122_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a122_values) <- c("age",1950:2080)
a122_values[,1] <- c(66:105)
a123_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a123_values) <- c("age",1950:2080)
a123_values[,1] <- c(66:105)
a124_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a124_values) <- c("age",1950:2080)
a124_values[,1] <- c(66:105)
a125_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a125_values) <- c("age",1950:2080)
a125_values[,1] <- c(66:105)
a126_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a126_values) <- c("age",1950:2080)
a126_values[,1] <- c(66:105)
a127_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a127_values) <- c("age",1950:2080)
a127_values[,1] <- c(66:105)
a12_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a12_global_values) <- c("age",1950:2080)
a12_global_values[,1] <- c(66:105)
RR_values <- matrix(c(0),
nrow=40,
ncol=2,
byrow=T);
colnames(RR_values) <- c("age","rr_DvsND")
RR_values[,1] <- c(66:105)
etat <- matrix(c(0), # initial state (non diseased)
nrow=nb_people,
ncol=105-65+1,
byrow=T);
colnames(etat) <- c(65:105);
# RUN 1
for (i in 1:nrow(etat)) {
alea0 <- runif(1, 0, 1);
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]) {
etat[i,1] <- "00" # non diseased and non exposed
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]) {
etat[i,1] <- "01" # non diseased and exposed 1
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]+data_prev_2_values[which(data_prev_2_values[,1]%in%(65) & data_prev_2_values[,3]%in%(gender)),2]) {
etat[i,1] <- "02" # non diseased and exposed 2
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]+data_prev_2_values[which(data_prev_2_values[,1]%in%(65) & data_prev_2_values[,3]%in%(gender)),2]+data_prev_3_values[which(data_prev_3_values[,1]%in%(65) & data_prev_3_values[,3]%in%(gender)),2]) {
etat[i,1] <- "03" # non diseased and exposed 3
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]+data_prev_2_values[which(data_prev_2_values[,1]%in%(65) & data_prev_2_values[,3]%in%(gender)),2]+data_prev_3_values[which(data_prev_3_values[,1]%in%(65) & data_prev_3_values[,3]%in%(gender)),2]+data_prev_4_values[which(data_prev_4_values[,1]%in%(65) & data_prev_4_values[,3]%in%(gender)),2]) {
etat[i,1] <- "04" # non diseased and exposed 4
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]+data_prev_2_values[which(data_prev_2_values[,1]%in%(65) & data_prev_2_values[,3]%in%(gender)),2]+data_prev_3_values[which(data_prev_3_values[,1]%in%(65) & data_prev_3_values[,3]%in%(gender)),2]+data_prev_4_values[which(data_prev_4_values[,1]%in%(65) & data_prev_4_values[,3]%in%(gender)),2]+data_prev_5_values[which(data_prev_5_values[,1]%in%(65) & data_prev_5_values[,3]%in%(gender)),2]) {
etat[i,1] <- "05" # non diseased and exposed 5
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]+data_prev_2_values[which(data_prev_2_values[,1]%in%(65) & data_prev_2_values[,3]%in%(gender)),2]+data_prev_3_values[which(data_prev_3_values[,1]%in%(65) & data_prev_3_values[,3]%in%(gender)),2]+data_prev_4_values[which(data_prev_4_values[,1]%in%(65) & data_prev_4_values[,3]%in%(gender)),2]+data_prev_5_values[which(data_prev_5_values[,1]%in%(65) & data_prev_5_values[,3]%in%(gender)),2]+data_prev_6_values[which(data_prev_6_values[,1]%in%(65) & data_prev_6_values[,3]%in%(gender)),2]) {
etat[i,1] <- "06" # non diseased and exposed 6
} else {
etat[i,1] <- "07" # non diseased and exposed 7
}
}
}
}
}
}
}
};
for (j in 2:ncol(etat)) { # for each people
for (i in 1:nrow(etat)) { # for each age
alea <- runif(1, 0, 1);
alea0 <- runif(1, 0, 1);
if (etat[i,j-1] == "00") {
if (alea0 <= data_incid_0_values[which(data_incid_0_values[,1]%in%(j-1+65) & data_incid_0_values[,3]%in%(gender)),2]) {
etat[i,j] <- "02"; # non diseased and exposed 2
} else {
etat[i,j] <- "00"; # non diseased and non exposed
}
} else {
if (etat[i,j-1] == "01") {
if (alea0 <= data_incid_1_values[which(data_incid_1_values[,1]%in%(j-1+65) & data_incid_1_values[,3]%in%(gender)),2]) {
etat[i,j] <- "04"; # non diseased and exposed 4
} else {
etat[i,j] <- "01"; # non diseased and exposed 1
}
} else {
if (etat[i,j-1] == "03") {
if (alea0 <= data_incid_3_values[which(data_incid_3_values[,1]%in%(j-1+65) & data_incid_3_values[,3]%in%(gender)),2]) {
etat[i,j] <- "06"; # non diseased and exposed 6
} else {
etat[i,j] <- "03"; # non diseased and exposed 3
}
} else {
if (etat[i,j-1] == "05") {
if (alea0 <= data_incid_5_values[which(data_incid_5_values[,1]%in%(j-1+65) & data_incid_5_values[,3]%in%(gender)),2]) {
etat[i,j] <- "07"; # non diseased and exposed 7
} else {
etat[i,j] <- "05"; # non diseased and exposed 5
}
} else {
if (etat[i,j-1] == "02") {
etat[i,j] <- "02"; # non diseased and exposed 2
} else {
if (etat[i,j-1] == "04") {
etat[i,j] <- "04"; # non diseased and exposed 4
} else {
if (etat[i,j-1] == "06") {
etat[i,j] <- "06"; # non diseased and exposed 6
} else {
if (etat[i,j-1] == "07") {
etat[i,j] <- "07"; # non diseased and exposed 7
} else {
if (etat[i,j-1] == "10") {
if (alea0 <= data_incid_0_values[which(data_incid_0_values[,1]%in%(j-1+65) & data_incid_0_values[,3]%in%(gender)),2]) {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
etat[i,j] <- "10"; # diseased and non exposed
}
} else {
if (etat[i,j-1] == "11") {
if (alea0 <= data_incid_1_values[which(data_incid_1_values[,1]%in%(j-1+65) & data_incid_1_values[,3]%in%(gender)),2]) {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
etat[i,j] <- "11"; # diseased and exposed 1
}
} else {
if (etat[i,j-1] == "13") {
if (alea0 <= data_incid_3_values[which(data_incid_3_values[,1]%in%(j-1+65) & data_incid_3_values[,3]%in%(gender)),2]) {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
etat[i,j] <- "13"; # diseased and exposed 3
}
} else {
if (etat[i,j-1] == "15") {
if (alea0 <= data_incid_5_values[which(data_incid_5_values[,1]%in%(j-1+65) & data_incid_5_values[,3]%in%(gender)),2]) {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- "15"; # diseased and exposed 5
}
} else {
if (etat[i,j-1] == "12") {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
if (etat[i,j-1] == "14") {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
if (etat[i,j-1] == "16") {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
if (etat[i,j-1] == "17") {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- etat[i,j-1]
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
if (sum(etat[,j]%in%("00"))!=0) {
data_prev_0_values_ND[which(data_prev_0_values_ND[,1]%in%(j+64) & data_prev_0_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("00"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("10"))!=0) {
data_prev_0_values_D[which(data_prev_0_values_D[,1]%in%(j+64) & data_prev_0_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("10"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("01"))!=0) {
data_prev_1_values_ND[which(data_prev_1_values_ND[,1]%in%(j+64) & data_prev_1_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("01"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("11"))!=0) {
data_prev_1_values_D[which(data_prev_1_values_D[,1]%in%(j+64) & data_prev_1_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("11"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("02"))!=0) {
data_prev_2_values_ND[which(data_prev_2_values_ND[,1]%in%(j+64) & data_prev_2_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("02"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("12"))!=0) {
data_prev_2_values_D[which(data_prev_2_values_D[,1]%in%(j+64) & data_prev_2_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("12"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("03"))!=0) {
data_prev_3_values_ND[which(data_prev_3_values_ND[,1]%in%(j+64) & data_prev_3_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("03"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("13"))!=0) {
data_prev_3_values_D[which(data_prev_3_values_D[,1]%in%(j+64) & data_prev_3_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("13"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("04"))!=0) {
data_prev_4_values_ND[which(data_prev_4_values_ND[,1]%in%(j+64) & data_prev_4_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("04"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("14"))!=0) {
data_prev_4_values_D[which(data_prev_4_values_D[,1]%in%(j+64) & data_prev_4_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("14"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("05"))!=0) {
data_prev_5_values_ND[which(data_prev_5_values_ND[,1]%in%(j+64) & data_prev_5_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("05"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("15"))!=0) {
data_prev_5_values_D[which(data_prev_5_values_D[,1]%in%(j+64) & data_prev_5_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("15"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("06"))!=0) {
data_prev_6_values_ND[which(data_prev_6_values_ND[,1]%in%(j+64) & data_prev_6_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("06"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("16"))!=0) {
data_prev_6_values_D[which(data_prev_6_values_D[,1]%in%(j+64) & data_prev_6_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("16"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("07"))!=0) {
data_prev_7_values_ND[which(data_prev_7_values_ND[,1]%in%(j+64) & data_prev_7_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("07"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("17"))!=0) {
data_prev_7_values_D[which(data_prev_7_values_D[,1]%in%(j+64) & data_prev_7_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("17"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
### Incidence of disease on non exposed peoples
for (a in 2:ncol(a010_values)){
a010_values[,a] <- as.numeric(data_a01_values[which(data_a01_values[,1] != 65 & data_a01_values[,2]%in%(gender)),a+1]) / (data_prev_0_values_ND[which(data_prev_0_values_ND[,1] != 65 & data_prev_0_values_ND[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_prev_1_values_ND[which(data_prev_1_values_ND[,1] != 65 & data_prev_1_values_ND[,3]%in%(gender)),2] + data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_prev_2_values_ND[which(data_prev_2_values_ND[,1] != 65 & data_prev_2_values_ND[,3]%in%(gender)),2] + data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_3_values_ND[which(data_prev_3_values_ND[,1] != 65 & data_prev_3_values_ND[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_prev_4_values_ND[which(data_prev_4_values_ND[,1] != 65 & data_prev_4_values_ND[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_5_values_ND[which(data_prev_5_values_ND[,1] != 65 & data_prev_5_values_ND[,3]%in%(gender)),2] + data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_6_values_ND[which(data_prev_6_values_ND[,1] != 65 & data_prev_6_values_ND[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_7_values_ND[which(data_prev_7_values_ND[,1] != 65 & data_prev_7_values_ND[,3]%in%(gender)),2]);
}
### Incidence of disease on exposed peoples
a011_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2];
a012_values[,-1] <- a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2];
a013_values[,-1] <- a010_values[,-1]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a014_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2];
a015_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a016_values[,-1] <- a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a017_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
### Global incidence of disease
a01_global_values[,-1] <- data_prev_0_values_ND[which(data_prev_0_values_ND[,1] != 65 & data_prev_0_values_ND[,3]%in%(gender)),2]*a010_values[,-1] + data_prev_1_values_ND[which(data_prev_1_values_ND[,1] != 65 & data_prev_1_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2] + data_prev_2_values_ND[which(data_prev_2_values_ND[,1] != 65 & data_prev_2_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2] + data_prev_3_values_ND[which(data_prev_3_values_ND[,1] != 65 & data_prev_3_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_4_values_ND[which(data_prev_4_values_ND[,1] != 65 & data_prev_4_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2] + data_prev_5_values_ND[which(data_prev_5_values_ND[,1] != 65 & data_prev_5_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_6_values_ND[which(data_prev_6_values_ND[,1] != 65 & data_prev_6_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_7_values_ND[which(data_prev_7_values_ND[,1] != 65 & data_prev_7_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
### Mortality of healthy subjects on non exposed peoples
for (a in 2:ncol(a020_values)){
a020_values[,a] <- as.numeric(data_a02_values[which(data_a02_values[,1] != 65 & data_a02_values[,2]%in%(gender)),a+1]) / (data_prev_0_values_ND[which(data_prev_0_values_ND[,1] != 65 & data_prev_0_values_ND[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_prev_1_values_ND[which(data_prev_1_values_ND[,1] != 65 & data_prev_1_values_ND[,3]%in%(gender)),2] + data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_prev_2_values_ND[which(data_prev_2_values_ND[,1] != 65 & data_prev_2_values_ND[,3]%in%(gender)),2] + data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_3_values_ND[which(data_prev_3_values_ND[,1] != 65 & data_prev_3_values_ND[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_prev_4_values_ND[which(data_prev_4_values_ND[,1] != 65 & data_prev_4_values_ND[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_5_values_ND[which(data_prev_5_values_ND[,1] != 65 & data_prev_5_values_ND[,3]%in%(gender)),2] + data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_6_values_ND[which(data_prev_6_values_ND[,1] != 65 & data_prev_6_values_ND[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_7_values_ND[which(data_prev_7_values_ND[,1] != 65 & data_prev_7_values_ND[,3]%in%(gender)),2]);
}
### Mortality of healthy subjects on exposed peoples
a021_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2];
a022_values[,-1] <- a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2];
a023_values[,-1] <- a020_values[,-1]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a024_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2];
a025_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a026_values[,-1] <- a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a027_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
### Global mortality of healthy subjects
a02_global_values[,-1] <- data_prev_0_values_ND[which(data_prev_0_values_ND[,1] != 65 & data_prev_0_values_ND[,3]%in%(gender)),2]*a020_values[,-1] + data_prev_1_values_ND[which(data_prev_1_values_ND[,1] != 65 & data_prev_1_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2] + data_prev_2_values_ND[which(data_prev_2_values_ND[,1] != 65 & data_prev_2_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2] + data_prev_3_values_ND[which(data_prev_3_values_ND[,1] != 65 & data_prev_3_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_4_values_ND[which(data_prev_4_values_ND[,1] != 65 & data_prev_4_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2] + data_prev_5_values_ND[which(data_prev_5_values_ND[,1] != 65 & data_prev_5_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_6_values_ND[which(data_prev_6_values_ND[,1] != 65 & data_prev_6_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_7_values_ND[which(data_prev_7_values_ND[,1] != 65 & data_prev_7_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
### Relative risks associated with the disease for mortality
RR_values[,2] <- data_rr_DvsND_values[which(data_rr_DvsND_values[,1] != 65 & data_rr_DvsND_values[,3]%in%(gender)),2];
### Mortality of diseased subjects on non exposed peoples
for (a in 2:ncol(a120_values)){
a120_values[,a] <- as.numeric(RR_values[,2])*as.numeric(data_a02_values[which(data_a02_values[,1] != 65 & data_a02_values[,2]%in%(gender)),a+1]) / (data_prev_0_values_D[which(data_prev_0_values_D[,1] != 65 & data_prev_0_values_D[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_prev_1_values_D[which(data_prev_1_values_D[,1] != 65 & data_prev_1_values_D[,3]%in%(gender)),2] + data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_prev_2_values_D[which(data_prev_2_values_D[,1] != 65 & data_prev_2_values_D[,3]%in%(gender)),2] + data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_3_values_D[which(data_prev_3_values_D[,1] != 65 & data_prev_3_values_D[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_prev_4_values_D[which(data_prev_4_values_D[,1] != 65 & data_prev_4_values_D[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_5_values_D[which(data_prev_5_values_D[,1] != 65 & data_prev_5_values_D[,3]%in%(gender)),2] + data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_6_values_D[which(data_prev_6_values_D[,1] != 65 & data_prev_6_values_D[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_7_values_D[which(data_prev_7_values_D[,1] != 65 & data_prev_7_values_D[,3]%in%(gender)),2]);
}
### Mortality of diseased subjects on exposed peoples
a121_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2];
a122_values[,-1] <- a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2];
a123_values[,-1] <- a120_values[,-1]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a124_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2];
a125_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a126_values[,-1] <- a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a127_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
### Global mortality of diseased subjects
a12_global_values[,-1] <- data_prev_0_values_D[which(data_prev_0_values_D[,1] != 65 & data_prev_0_values_D[,3]%in%(gender)),2]*a120_values[,-1] + data_prev_1_values_D[which(data_prev_1_values_D[,1] != 65 & data_prev_1_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2] + data_prev_2_values_D[which(data_prev_2_values_D[,1] != 65 & data_prev_2_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2] + data_prev_3_values_D[which(data_prev_3_values_D[,1] != 65 & data_prev_3_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_4_values_D[which(data_prev_4_values_D[,1] != 65 & data_prev_4_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2] + data_prev_5_values_D[which(data_prev_5_values_D[,1] != 65 & data_prev_5_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_6_values_D[which(data_prev_6_values_D[,1] != 65 & data_prev_6_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_7_values_D[which(data_prev_7_values_D[,1] != 65 & data_prev_7_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
for (i in 1:nrow(etat)) { # for each age
alea <- runif(1, 0, 1);
alea0 <- runif(1, 0, 1);
if (etat[i,j] == "00") {
a01 <- a010_values;
a02 <- a020_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "20"; # dead (with non exposed state)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "10"; # diseased and non exposed
} else {
etat[i,j] <- "00"; # non diseased and non exposed
}
}
} else {
if (etat[i,j] == "01") {
a01 <- a011_values;
a02 <- a021_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "21"; # dead (with exposed state 1)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "11"; # diseased and exposed 1
} else {
etat[i,j] <- "01"; # non diseased and exposed 1
}
}
} else {
if (etat[i,j] == "02") {
a01 <- a012_values;
a02 <- a022_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "22"; # dead (with exposed state 2)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
etat[i,j] <- "02"; # non diseased and exposed 2
}
}
} else {
if (etat[i,j] == "03") {
a01 <- a013_values;
a02 <- a023_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "23"; # dead (with exposed state 3)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "13"; # diseased and exposed 3
} else {
etat[i,j] <- "03"; # non diseased and exposed 3
}
}
} else {
if (etat[i,j] == "04") {
a01 <- a014_values;
a02 <- a024_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "24"; # dead (with exposed state 4)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
etat[i,j] <- "04"; # non diseased and exposed 4
}
};
} else {
if (etat[i,j] == "05") {
a01 <- a015_values;
a02 <- a025_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "25"; # dead (with exposed state 5)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "15"; # diseased and exposed 5
} else {
etat[i,j] <- "05"; # non diseased and exposed 5
}
}
} else {
if (etat[i,j] == "06") {
a01 <- a016_values;
a02 <- a026_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "26"; # dead (with exposed state 6)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
etat[i,j] <- "06"; # non diseased and exposed 6
}
};
} else {
if (etat[i,j] == "07") {
a01 <- a017_values;
a02 <- a027_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "27"; # dead (with exposed state 7)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- "07"; # non diseased and exposed 7
}
};
} else {
if (etat[i,j] == "10") {
a12 <- a120_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "20";
} else {
etat[i,j] <- "10";
}
} else {
if (etat[i,j] == "11") {
a12 <- a121_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "21";
} else {
etat[i,j] <- "11";
}
} else {
if (etat[i,j] == "12") {
a12 <- a122_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "22";
} else {
etat[i,j] <- "12";
}
} else {
if (etat[i,j] == "13") {
a12 <- a123_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "23";
} else {
etat[i,j] <- "13";
}
} else {
if (etat[i,j] == "14") {
a12 <- a124_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "24";
} else {
etat[i,j] <- "14";
}
} else {
if (etat[i,j] == "15") {
a12 <- a125_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "25";
} else {
etat[i,j] <- "15";
}
} else {
if (etat[i,j] == "16") {
a12 <- a126_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "26";
} else {
etat[i,j] <- "16";
}
} else {
if (etat[i,j] == "17") {
a12 <- a127_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "27";
} else {
etat[i,j] <- "17";
}
} else {
etat[i,j] <- etat[i,j]
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
# RUN 2
for (i in 1:nrow(etat)) {
if (intervention==1) {
if (annee < year_intervention) {
intervention_prev_0_values <- data_prev_0_values
intervention_prev_1_values <- data_prev_1_values
intervention_prev_2_values <- data_prev_2_values
intervention_prev_3_values <- data_prev_3_values
intervention_prev_4_values <- data_prev_4_values
intervention_prev_5_values <- data_prev_5_values
intervention_prev_6_values <- data_prev_6_values
intervention_prev_7_values <- data_prev_7_values
} else {
intervention_prev_0_values <- data_prev_0_values
intervention_prev_0_values[,2] <- 1
intervention_prev_1_values <- data_prev_1_values
intervention_prev_1_values[,2] <- 0
intervention_prev_2_values <- data_prev_2_values
intervention_prev_2_values[,2] <- 0
intervention_prev_3_values <- data_prev_3_values
intervention_prev_3_values[,2] <- 0
intervention_prev_4_values <- data_prev_4_values
intervention_prev_4_values[,2] <- 0
intervention_prev_5_values <- data_prev_5_values
intervention_prev_5_values[,2] <- 0
intervention_prev_6_values <- data_prev_6_values
intervention_prev_6_values[,2] <- 0
intervention_prev_7_values <- data_prev_7_values
intervention_prev_7_values[,2] <- 0
}
}
alea0 <- runif(1, 0, 1);
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]) {
etat[i,1] <- "00" # non diseased and non exposed
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]) {
etat[i,1] <- "01" # non diseased and exposed 1
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]+intervention_prev_2_values[which(intervention_prev_2_values[,1]%in%(65) & intervention_prev_2_values[,3]%in%(gender)),2]) {
etat[i,1] <- "02" # non diseased and exposed 2
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]+intervention_prev_2_values[which(intervention_prev_2_values[,1]%in%(65) & intervention_prev_2_values[,3]%in%(gender)),2]+intervention_prev_3_values[which(intervention_prev_3_values[,1]%in%(65) & intervention_prev_3_values[,3]%in%(gender)),2]) {
etat[i,1] <- "03" # non diseased and exposed 3
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]+intervention_prev_2_values[which(intervention_prev_2_values[,1]%in%(65) & intervention_prev_2_values[,3]%in%(gender)),2]+intervention_prev_3_values[which(intervention_prev_3_values[,1]%in%(65) & intervention_prev_3_values[,3]%in%(gender)),2]+intervention_prev_4_values[which(intervention_prev_4_values[,1]%in%(65) & intervention_prev_4_values[,3]%in%(gender)),2]) {
etat[i,1] <- "04" # non diseased and exposed 4
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]+intervention_prev_2_values[which(intervention_prev_2_values[,1]%in%(65) & intervention_prev_2_values[,3]%in%(gender)),2]+intervention_prev_3_values[which(intervention_prev_3_values[,1]%in%(65) & intervention_prev_3_values[,3]%in%(gender)),2]+intervention_prev_4_values[which(intervention_prev_4_values[,1]%in%(65) & intervention_prev_4_values[,3]%in%(gender)),2]+intervention_prev_5_values[which(intervention_prev_5_values[,1]%in%(65) & intervention_prev_5_values[,3]%in%(gender)),2]) {
etat[i,1] <- "05" # non diseased and exposed 5
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]+intervention_prev_2_values[which(intervention_prev_2_values[,1]%in%(65) & intervention_prev_2_values[,3]%in%(gender)),2]+intervention_prev_3_values[which(intervention_prev_3_values[,1]%in%(65) & intervention_prev_3_values[,3]%in%(gender)),2]+intervention_prev_4_values[which(intervention_prev_4_values[,1]%in%(65) & intervention_prev_4_values[,3]%in%(gender)),2]+intervention_prev_5_values[which(intervention_prev_5_values[,1]%in%(65) & intervention_prev_5_values[,3]%in%(gender)),2]+intervention_prev_6_values[which(intervention_prev_6_values[,1]%in%(65) & intervention_prev_6_values[,3]%in%(gender)),2]) {
etat[i,1] <- "06" # non diseased and exposed 6
} else {
etat[i,1] <- "07" # non diseased and exposed 7
}
}
}
}
}
}
}
};
for (j in 2:ncol(etat)) { # for each people
if (intervention==1) {
if (annee+j-1 < year_intervention) {
intervention_incid_0_values <- data_incid_0_values
intervention_incid_1_values <- data_incid_1_values
intervention_incid_3_values <- data_incid_3_values
intervention_incid_5_values <- data_incid_5_values
} else {
intervention_incid_0_values <- data_incid_0_values
intervention_incid_0_values[,2] <- 0
intervention_incid_1_values <- data_incid_1_values
intervention_incid_1_values[,2] <- 0
intervention_incid_3_values <- data_incid_3_values
intervention_incid_3_values[,2] <- 0
intervention_incid_5_values <- data_incid_5_values
intervention_incid_5_values[,2] <- 0
}
}
for (i in 1:nrow(etat)) { # for each age
alea <- runif(1, 0, 1);
alea0 <- runif(1, 0, 1);
if (etat[i,j-1] == "00") {
if (alea0 <= intervention_incid_0_values[which(intervention_incid_0_values[,1]%in%(j-1+65) & intervention_incid_0_values[,3]%in%(gender)),2]) {
etat[i,j] <- "02"; # non diseased and exposed 2
} else {
etat[i,j] <- "00"; # non diseased and non exposed
}
} else {
if (etat[i,j-1] == "01") {
if (alea0 <= intervention_incid_1_values[which(intervention_incid_1_values[,1]%in%(j-1+65) & intervention_incid_1_values[,3]%in%(gender)),2]) {
etat[i,j] <- "04"; # non diseased and exposed 4
} else {
etat[i,j] <- "01"; # non diseased and exposed 1
}
} else {
if (etat[i,j-1] == "03") {
if (alea0 <= intervention_incid_3_values[which(intervention_incid_3_values[,1]%in%(j-1+65) & intervention_incid_3_values[,3]%in%(gender)),2]) {
etat[i,j] <- "06"; # non diseased and exposed 6
} else {
etat[i,j] <- "03"; # non diseased and exposed 3
}
} else {
if (etat[i,j-1] == "05") {
if (alea0 <= intervention_incid_5_values[which(intervention_incid_5_values[,1]%in%(j-1+65) & intervention_incid_5_values[,3]%in%(gender)),2]) {
etat[i,j] <- "07"; # non diseased and exposed 7
} else {
etat[i,j] <- "05"; # non diseased and exposed 5
}
} else {
if (etat[i,j-1] == "02") {
etat[i,j] <- "02"; # non diseased and exposed 2
} else {
if (etat[i,j-1] == "04") {
etat[i,j] <- "04"; # non diseased and exposed 4
} else {
if (etat[i,j-1] == "06") {
etat[i,j] <- "06"; # non diseased and exposed 6
} else {
if (etat[i,j-1] == "07") {
etat[i,j] <- "07"; # non diseased and exposed 7
} else {
if (etat[i,j-1] == "10") {
if (alea0 <= intervention_incid_0_values[which(intervention_incid_0_values[,1]%in%(j-1+65) & intervention_incid_0_values[,3]%in%(gender)),2]) {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
etat[i,j] <- "10"; # diseased and non exposed
}
} else {
if (etat[i,j-1] == "11") {
if (alea0 <= intervention_incid_1_values[which(intervention_incid_1_values[,1]%in%(j-1+65) & intervention_incid_1_values[,3]%in%(gender)),2]) {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
etat[i,j] <- "11"; # diseased and exposed 1
}
} else {
if (etat[i,j-1] == "13") {
if (alea0 <= intervention_incid_3_values[which(intervention_incid_3_values[,1]%in%(j-1+65) & intervention_incid_3_values[,3]%in%(gender)),2]) {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
etat[i,j] <- "13"; # diseased and exposed 3
}
} else {
if (etat[i,j-1] == "15") {
if (alea0 <= intervention_incid_5_values[which(intervention_incid_5_values[,1]%in%(j-1+65) & intervention_incid_5_values[,3]%in%(gender)),2]) {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- "15"; # diseased and exposed 5
}
} else {
if (etat[i,j-1] == "12") {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
if (etat[i,j-1] == "14") {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
if (etat[i,j-1] == "16") {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
if (etat[i,j-1] == "17") {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- etat[i,j-1]
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
for (i in 1:nrow(etat)) { # for each age
alea <- runif(1, 0, 1);
alea0 <- runif(1, 0, 1);
if (etat[i,j] == "00") {
a01 <- a010_values;
a02 <- a020_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "20"; # dead (with non exposed state)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "10"; # diseased and non exposed
} else {
etat[i,j] <- "00"; # non diseased and non exposed
}
}
} else {
if (etat[i,j] == "01") {
a01 <- a011_values;
a02 <- a021_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "21"; # dead (with exposed state 1)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "11"; # diseased and exposed 1
} else {
etat[i,j] <- "01"; # non diseased and exposed 1
}
}
} else {
if (etat[i,j] == "02") {
a01 <- a012_values;
a02 <- a022_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "22"; # dead (with exposed state 2)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
etat[i,j] <- "02"; # non diseased and exposed 2
}
}
} else {
if (etat[i,j] == "03") {
a01 <- a013_values;
a02 <- a023_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "23"; # dead (with exposed state 3)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "13"; # diseased and exposed 3
} else {
etat[i,j] <- "03"; # non diseased and exposed 3
}
}
} else {
if (etat[i,j] == "04") {
a01 <- a014_values;
a02 <- a024_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "24"; # dead (with exposed state 4)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
etat[i,j] <- "04"; # non diseased and exposed 4
}
};
} else {
if (etat[i,j] == "05") {
a01 <- a015_values;
a02 <- a025_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "25"; # dead (with exposed state 5)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "15"; # diseased and exposed 5
} else {
etat[i,j] <- "05"; # non diseased and exposed 5
}
}
} else {
if (etat[i,j] == "06") {
a01 <- a016_values;
a02 <- a026_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "26"; # dead (with exposed state 6)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
etat[i,j] <- "06"; # non diseased and exposed 6
}
};
} else {
if (etat[i,j] == "07") {
a01 <- a017_values;
a02 <- a027_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "27"; # dead (with exposed state 7)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- "07"; # non diseased and exposed 7
}
};
} else {
if (etat[i,j] == "10") {
a12 <- a120_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "20";
} else {
etat[i,j] <- "10";
}
} else {
if (etat[i,j] == "11") {
a12 <- a121_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "21";
} else {
etat[i,j] <- "11";
}
} else {
if (etat[i,j] == "12") {
a12 <- a122_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "22";
} else {
etat[i,j] <- "12";
}
} else {
if (etat[i,j] == "13") {
a12 <- a123_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "23";
} else {
etat[i,j] <- "13";
}
} else {
if (etat[i,j] == "14") {
a12 <- a124_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "24";
} else {
etat[i,j] <- "14";
}
} else {
if (etat[i,j] == "15") {
a12 <- a125_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "25";
} else {
etat[i,j] <- "15";
}
} else {
if (etat[i,j] == "16") {
a12 <- a126_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "26";
} else {
etat[i,j] <- "16";
}
} else {
if (etat[i,j] == "17") {
a12 <- a127_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "27";
} else {
etat[i,j] <- "17";
}
} else {
etat[i,j] <- etat[i,j]
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
### Computation of health indicators :
### Overall life-expectancy
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")))
};
esp_vie_gen[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Overall life-expectancy on exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("01") | etat[,age-64]%in%("11")),age-64]%in%("01") | etat[which(etat[,age-64]%in%("01") | etat[,age-64]%in%("11")),age-64]%in%("11"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("01") | etat[,age-64]%in%("11")),j]%in%("01") | etat[which(etat[,age-64]%in%("01") | etat[,age-64]%in%("11")),j]%in%("11"))
};
esp_vie_gen_conso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Overall life-expectancy on non exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),age-64]%in%("00") | etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),age-64]%in%("10"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),j]%in%("00") | etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),j]%in%("10") | etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),j]%in%("01") | etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),j]%in%("11"))
};
esp_vie_gen_nonconso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy without disease
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07"))),age-64]%in%(c("00","01","02","03","04","05","06","07")))
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07"))),j]%in%(c("00","01","02","03","04","05","06","07")))
};
esp_vie_sans_mal[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy without disease on exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("01")),age-64]%in%("01"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("01")),j]%in%("01"))
};
esp_vie_sans_mal_conso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy without disease on non exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("00")),age-64]%in%("00"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("00")),j]%in%("00") | etat[which(etat[,age-64]%in%("00")),j]%in%("01"))
};
esp_vie_sans_mal_nonconso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for diseased subject
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("10") | etat[,age-64]%in%("11")),age-64]%in%("10") | etat[which(etat[,age-64]%in%("10") | etat[,age-64]%in%("11")),age-64]%in%("11"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("10") | etat[,age-64]%in%("11")),j]%in%("10") | etat[which(etat[,age-64]%in%("10") | etat[,age-64]%in%("11")),j]%in%("11"))
};
esp_vie_mal[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for diseased subject on exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("11")),age-64]%in%("11"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("11")),j]%in%("11"))
};
esp_vie_mal_conso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for diseased subject on non exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("10")),age-64]%in%("10"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("10")),j]%in%("10") | etat[which(etat[,age-64]%in%("10")),j]%in%("11"))
};
esp_vie_mal_nonconso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for non diseased subject
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07"))),age-64]%in%(c("00","01","02","03","04","05","06","07")));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07"))),j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")))
};
esp_vie_non_mal[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for non diseased subject on exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("01")),age-64]%in%("01"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("01")),j]%in%("01") | etat[which(etat[,age-64]%in%("01")),j]%in%("11"))
};
esp_vie_non_mal_conso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for non diseased subject on non exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("00")),age-64]%in%("00"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("00")),j]%in%("00") | etat[which(etat[,age-64]%in%("00")),j]%in%("01") | etat[which(etat[,age-64]%in%("00")),j]%in%("10") | etat[which(etat[,age-64]%in%("00")),j]%in%("11"))
};
esp_vie_non_mal_nonconso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Prevalence of disease
if (age > 65 & age < 100) {
d0 <- sum(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17"))) + 0.5 * sum((etat[,age-64]%in%(c("20","21","22","23","24","25","26","27"))) & (etat[,age-65]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17"))));
s0 <- sum(etat[,1]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum((etat[,age-64]%in%(c("10","11","12","13","14","15","16","17"))) & (etat[,age-65]%in%(c("10","11","12","13","14","15","16","17")))) + 0.5 * sum((etat[,age-64]%in%(c("10","11","12","13","14","15","16","17"))) & (etat[,age-65]%in%(c("00","01","02","03","04","05","06","07")))) + 0.5 * sum((etat[,age-64]%in%(c("20","21","22","23","24","25","26","27"))) & (etat[,age-65]%in%(c("10","11","12","13","14","15","16","17"))));
if (s0 != 0) {
p01 <- s1/s0;
taux_prevalence[age-64,2] <- s1/d0;
nb <- p01*data_pop[which(data_pop[,1]%in%(an0) & data_pop[,3]%in%(gender)),2];
prevalence[age-64,2] <- nb;
};
};
### Survival
if (age == 65) {
survie[age-64,2] <- data_pop[which(data_pop[,1]%in%(an0) & data_pop[,3]%in%(gender)),2]
};
if (age > 65 & age < 100) {
d0 <- sum(etat[,age-65]%in%("00") | etat[,age-65]%in%("01") | etat[,age-65]%in%("10") | etat[,age-65]%in%("11"));
s0 <- sum(etat[,1]%in%("00") | etat[,1]%in%("01") | etat[,1]%in%("10") | etat[,1]%in%("11"));
s1 <- sum(etat[,age-64]%in%("00") | etat[,age-64]%in%("01") | etat[,age-64]%in%("10") | etat[,age-64]%in%("11"));
if (s0 != 0) {
p01 <- s1/s0;
taux_survivants[age-64,2] <- s1/d0;
nb <- p01*data_pop[which(data_pop[,1]%in%(an0) & data_pop[,3]%in%(gender)),2];
survie[age-64,2] <- nb;
};
};
### Mean number of years spent with disease
if (age < 101) {
nb_moy_dem[age-64,2] <- esp_vie_non_mal[age-64,2] - esp_vie_sans_mal[age-64,2]
} else {
n0 <- NA
};
### Mean number of years spent with disease on exposed peoples
if (age < 101) {
nb_moy_dem_conso[age-64,2] <- esp_vie_non_mal_conso[age-64,2] - esp_vie_sans_mal_conso[age-64,2];
} else {
n0 <- NA;
};
### Mean number of years spent with disease on non exposed peoples
if (age < 101) {
nb_moy_dem_nonconso[age-64,2] <- esp_vie_non_mal_nonconso[age-64,2] - esp_vie_sans_mal_nonconso[age-64,2];
} else {
n0 <- NA;
};
### Life-long probability of disease
if (age == 65) {
for (i in (age-63):nrow(prb_dem)) {
prb_dem[i,2] <- sum(etat[which(etat[,i-1]%in%(c("00","01","02","03","04","05","06","07"))),i]%in%(c("10","11","12","13","14","15","16","17")))
}
};
### Average age at disease onset
if (age == 65) {
for (i in (age-63):nrow(age_dem)) {
age_dem[i,2] <- sum(etat[which(etat[,i-1]%in%(c("00","01","02","03","04","05","06","07"))),i]%in%(c("10","11","12","13","14","15","16","17")))
}
};
### Mean number of years of exposition
if (age == 65) {
for (i in (age-63):nrow(age_dem)) {
moy_conso[i,2] <- sum(etat[which(etat[,1]%in%("00") | etat[,1]%in%("10")),i]%in%("01") | etat[which(etat[,1]%in%("00") | etat[,1]%in%("10")),i]%in%("11"))
}
};
### Number of exposed peoples at least one time
if (age <= 105) {
s0 <- sum(etat[,age-64]%in%("00") | etat[,age-64]%in%("10") | etat[,age-64]%in%("01") | etat[,age-64]%in%("11"));
s1 <- sum(etat[,age-64]%in%("01") | etat[,age-64]%in%("11"));
if (s0 != 0) {
prevalence_conso[age-64,2] <- s1/s0;
} else {
prevalence_conso[age-64,2] <- NA;
};
};
### Mortality rate
if (age > 65 & age < 100) {
s0 <- sum(etat[,age-64]%in%("00") | etat[,age-64]%in%("10") | etat[,age-64]%in%("01") | etat[,age-64]%in%("11"));
s1 <- sum(etat[,age-64]%in%("20") | etat[,age-64]%in%("21")) - sum(etat[,age-65]%in%("20") | etat[,age-65]%in%("21"));
if (s0 != 0) {
quotient_mortalite[age-64,2] <- s1/s0;
} else {
quotient_mortalite[age-64,2] <- NA;
};
};
##############################
########## ESSAIS ############
##############################
### Dementia incidence
if (age >= 65) {
for (j in 1:nrow(incid_demence)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")));
s1 <- sum((etat[,j+1]%in%(c("10","11","12","13","14","15","16","17"))) & (etat[,j]%in%(c("00","01","02","03","04","05","06","07"))));
if (s0 != 0) {
incid_demence[j,age-63] <- s1/s0;
};
}
};
### Mortality incidence
if (age >= 65) {
for (j in 1:nrow(incid_mort_NoD)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")));
s1 <- sum((etat[,j+1]%in%(c("20","21","22","23","24","25","26","27"))) & (etat[,j]%in%(c("00","01","02","03","04","05","06","07"))));
if (s0 != 0) {
incid_mort_NoD[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(incid_mort_Dem)) {
s0 <- sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")));
s1 <- sum((etat[,j+1]%in%(c("20","21","22","23","24","25","26","27"))) & (etat[,j]%in%(c("10","11","12","13","14","15","16","17"))));
if (s0 != 0) {
incid_mort_Dem[j,age-63] <- s1/s0;
};
}
};
### Non exposed prevalence
if (age >= 65) {
for (j in 1:nrow(prevalence_noe)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("00","10")));
if (s0 != 0) {
prevalence_noe[j,age-63] <- s1/s0;
};
}
};
### Hypertension prevalence
if (age >= 65) {
for (j in 1:nrow(prevalence_hta)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("01","04","05","07","11","14","15","17")));
if (s0 != 0) {
prevalence_hta[j,age-63] <- s1/s0;
};
}
};
### Physical inactivity prevalence
if (age >= 65) {
for (j in 1:nrow(prevalence_ina)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("03","05","06","07","13","15","16","17")));
if (s0 != 0) {
prevalence_ina[j,age-63] <- s1/s0;
};
}
};
### Diabete prevalence
if (age >= 65) {
for (j in 1:nrow(prevalence_dia)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("02","04","06","07","12","14","16","17")));
if (s0 != 0) {
prevalence_dia[j,age-63] <- s1/s0;
};
}
};
### Expositions prevalence
if (age >= 65) {
for (j in 1:nrow(prevalence_exp0)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("00","10")));
if (s0 != 0) {
prevalence_exp0[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp1)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("01","11")));
if (s0 != 0) {
prevalence_exp1[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp2)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("02","12")));
if (s0 != 0) {
prevalence_exp2[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp3)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("03","13")));
if (s0 != 0) {
prevalence_exp3[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp4)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("04","14")));
if (s0 != 0) {
prevalence_exp4[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp5)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("05","15")));
if (s0 != 0) {
prevalence_exp5[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp6)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("06","16")));
if (s0 != 0) {
prevalence_exp6[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp7)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("07","17")));
if (s0 != 0) {
prevalence_exp7[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prop_dem_global)) {
s0 <- sum(etat[,j+1]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j+1]%in%(c("10","11","12","13","14","15","16","17")));
if (s0 != 0) {
prop_dem_global[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prop_dem_diabet)) {
s0 <- sum(etat[,j+1]%in%(c("02","04","06","07","12","14","16","17")));
s1 <- sum(etat[,j+1]%in%(c("12","14","16","17")));
if (s0 != 0) {
prop_dem_diabet[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prop_dem_hypert)) {
s0 <- sum(etat[,j+1]%in%(c("01","04","05","07","11","14","15","17")));
s1 <- sum(etat[,j+1]%in%(c("11","14","15","17")));
if (s0 != 0) {
prop_dem_hypert[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prop_dem_inacti)) {
s0 <- sum(etat[,j+1]%in%(c("03","05","06","07","13","15","16","17")));
s1 <- sum(etat[,j+1]%in%(c("13","15","16","17")));
if (s0 != 0) {
prop_dem_inacti[j,age-63] <- s1/s0;
};
}
};
}
### Computation for variability
if (nb_iter != 0) {
indicateurs <- varHI(t = t,
intervention = intervention,
year_intervention = year_intervention,
nb_people = nb_people,
nb_iter = nb_iter,
data_pop = data_pop,
gender = gender,
data_prev = data_prev,
data_incid = data_incid,
a010 = a010,
a011 = a011,
a01_global = a01_global,
a020 = a020,
a021 = a021,
a02_global = a02_global,
a120 = a120,
a121 = a121,
a12_global = a12_global,
data_theta01 = data_theta01,
data_theta02 = data_theta02,
data_theta12 = data_theta12,
RR = RR,
prb_dem = prb_dem,
age_dem = age_dem,
Ncpus = Ncpus)
for (i in 1:nb_iter) {
esp_vie_gen[,i+2] <- indicateurs[,i]$LE_overall
esp_vie_gen_conso[,i+2] <- indicateurs[,i]$LE_overall_exp
esp_vie_gen_nonconso[,i+2] <- indicateurs[,i]$LE_overall_nonexp
esp_vie_sans_mal[,i+2] <- indicateurs[,i]$LE_without_dis
esp_vie_sans_mal_conso[,i+2] <- indicateurs[,i]$LE_without_dis_exp
esp_vie_sans_mal_nonconso[,i+2] <- indicateurs[,i]$LE_without_dis_nonexp
esp_vie_mal[,i+2] <- indicateurs[,i]$LE_dis
esp_vie_mal_conso[,i+2] <- indicateurs[,i]$LE_dis_exp
esp_vie_mal_nonconso[,i+2] <- indicateurs[,i]$LE_dis_nonexp
esp_vie_non_mal[,i+2] <- indicateurs[,i]$LE_non_dis
esp_vie_non_mal_conso[,i+2] <- indicateurs[,i]$LE_non_dis_exp
esp_vie_non_mal_nonconso[,i+2] <- indicateurs[,i]$LE_non_dis_nonexp
prevalence[,i+2] <- indicateurs[,i]$np_age
taux_prevalence[,i+2] <- indicateurs[,i]$tp_dis
survie[,i+2] <- indicateurs[,i]$nsurvival
taux_survivants[,i+2] <- indicateurs[,i]$rsurvival
nb_moy_dem[,i+2] <- indicateurs[,i]$nb_dis
nb_moy_dem_conso [,i+2] <- indicateurs[,i]$nb_dis_exp
nb_moy_dem_nonconso [,i+2] <- indicateurs[,i]$nb_dis_nonexp
prb_dem[,i+2] <- indicateurs[,i]$p_dis
age_dem[,i+2] <- indicateurs[,i]$a_dis
moy_conso[,i+2] <- indicateurs[,i]$m_exp
prevalence_conso[,i+2] <- indicateurs[,i]$p_exp
quotient_mortalite[,i+2] <- indicateurs[,i]$mortality_r
}
}
### Computation of health indicators :
### Overall life-expectancy
life_expectancy <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy[,1] <- esp_vie_gen[,1]
life_expectancy[,2] <- esp_vie_gen[,2]
life_expectancy[,3] <- apply(esp_vie_gen[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy[,4] <- life_expectancy[,2] - 1.96*life_expectancy[,3];
life_expectancy[,5] <- life_expectancy[,2] + 1.96*life_expectancy[,3];
for (i in 1:nrow(life_expectancy)) {
if (life_expectancy[i,4]<0 & is.na(life_expectancy[i,4])==F) {
life_expectancy[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy)) {
if (life_expectancy[i,2]==0 & life_expectancy[i,4]==0 & life_expectancy[i,5]==0 &
is.na(life_expectancy[i,2])==F & is.na(life_expectancy[i,4])==F & is.na(life_expectancy[i,5])==F) {
life_expectancy[i,2] <- NA;
life_expectancy[i,3] <- NA;
life_expectancy[i,4] <- NA;
life_expectancy[i,5] <- NA
}
}
colnames(life_expectancy) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Overall life-expectancy on exposed peoples
life_expectancy_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_exp[,1] <- esp_vie_gen_conso[,1]
life_expectancy_exp[,2] <- esp_vie_gen_conso[,2]
life_expectancy_exp[,3] <- apply(esp_vie_gen_conso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_exp[,4] <- life_expectancy_exp[,2] - 1.96*life_expectancy_exp[,3];
life_expectancy_exp[,5] <- life_expectancy_exp[,2] + 1.96*life_expectancy_exp[,3];
for (i in 1:nrow(life_expectancy_exp)) {
if (life_expectancy_exp[i,4]<0 & is.na(life_expectancy_exp[i,4])==F) {
life_expectancy_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_exp)) {
if (life_expectancy_exp[i,2]==0 & life_expectancy_exp[i,4]==0 & life_expectancy_exp[i,5]==0 &
is.na(life_expectancy_exp[i,2])==F & is.na(life_expectancy_exp[i,4])==F & is.na(life_expectancy_exp[i,5])==F) {
life_expectancy_exp[i,2] <- NA;
life_expectancy_exp[i,3] <- NA;
life_expectancy_exp[i,4] <- NA;
life_expectancy_exp[i,5] <- NA
}
}
colnames(life_expectancy_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Overall life-expectancy on non exposed peoples
life_expectancy_n_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_n_exp[,1] <- esp_vie_gen_nonconso[,1]
life_expectancy_n_exp[,2] <- esp_vie_gen_nonconso[,2]
life_expectancy_n_exp[,3] <- apply(esp_vie_gen_nonconso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_n_exp[,4] <- life_expectancy_n_exp[,2] - 1.96*life_expectancy_n_exp[,3];
life_expectancy_n_exp[,5] <- life_expectancy_n_exp[,2] + 1.96*life_expectancy_n_exp[,3];
for (i in 1:nrow(life_expectancy_n_exp)) {
if (life_expectancy_n_exp[i,4]<0 & is.na(life_expectancy_n_exp[i,4])==F) {
life_expectancy_n_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_n_exp)) {
if (life_expectancy_n_exp[i,2]==0 & life_expectancy_n_exp[i,4]==0 & life_expectancy_n_exp[i,5]==0 &
is.na(life_expectancy_n_exp[i,2])==F & is.na(life_expectancy_n_exp[i,4])==F & is.na(life_expectancy_n_exp[i,5])==F) {
life_expectancy_n_exp[i,2] <- NA;
life_expectancy_n_exp[i,3] <- NA;
life_expectancy_n_exp[i,4] <- NA;
life_expectancy_n_exp[i,5] <- NA
}
}
colnames(life_expectancy_n_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy without disease
life_expectancy_w_dis <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_w_dis[,1] <- esp_vie_sans_mal[,1]
life_expectancy_w_dis[,2] <- esp_vie_sans_mal[,2]
life_expectancy_w_dis[,3] <- apply(esp_vie_sans_mal[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_w_dis[,4] <- life_expectancy_w_dis[,2] - 1.96*life_expectancy_w_dis[,3];
life_expectancy_w_dis[,5] <- life_expectancy_w_dis[,2] + 1.96*life_expectancy_w_dis[,3];
for (i in 1:nrow(life_expectancy_w_dis)) {
if (life_expectancy_w_dis[i,4]<0 & is.na(life_expectancy_w_dis[i,4])==F) {
life_expectancy_w_dis[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_w_dis)) {
if (life_expectancy_w_dis[i,2]==0 & life_expectancy_w_dis[i,4]==0 & life_expectancy_w_dis[i,5]==0 &
is.na(life_expectancy_w_dis[i,2])==F & is.na(life_expectancy_w_dis[i,4])==F & is.na(life_expectancy_w_dis[i,5])==F) {
life_expectancy_w_dis[i,2] <- NA;
life_expectancy_w_dis[i,3] <- NA;
life_expectancy_w_dis[i,4] <- NA;
life_expectancy_w_dis[i,5] <- NA
}
}
colnames(life_expectancy_w_dis) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy without disease on exposed peoples
life_expectancy_w_dis_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_w_dis_exp[,1] <- esp_vie_sans_mal_conso[,1]
life_expectancy_w_dis_exp[,2] <- esp_vie_sans_mal_conso[,2]
life_expectancy_w_dis_exp[,3] <- apply(esp_vie_sans_mal_conso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_w_dis_exp[,4] <- life_expectancy_w_dis_exp[,2] - 1.96*life_expectancy_w_dis_exp[,3];
life_expectancy_w_dis_exp[,5] <- life_expectancy_w_dis_exp[,2] + 1.96*life_expectancy_w_dis_exp[,3];
for (i in 1:nrow(life_expectancy_w_dis_exp)) {
if (life_expectancy_w_dis_exp[i,4]<0 & is.na(life_expectancy_w_dis_exp[i,4])==F) {
life_expectancy_w_dis_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_w_dis_exp)) {
if (life_expectancy_w_dis_exp[i,2]==0 & life_expectancy_w_dis_exp[i,4]==0 & life_expectancy_w_dis_exp[i,5]==0 &
is.na(life_expectancy_w_dis_exp[i,2])==F & is.na(life_expectancy_w_dis_exp[i,4])==F & is.na(life_expectancy_w_dis_exp[i,5])==F) {
life_expectancy_w_dis_exp[i,2] <- NA;
life_expectancy_w_dis_exp[i,3] <- NA;
life_expectancy_w_dis_exp[i,4] <- NA;
life_expectancy_w_dis_exp[i,5] <- NA
}
}
colnames(life_expectancy_w_dis_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy without disease on non exposed peoples
life_expectancy_w_dis_n_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_w_dis_n_exp[,1] <- esp_vie_sans_mal_nonconso[,1]
life_expectancy_w_dis_n_exp[,2] <- esp_vie_sans_mal_nonconso[,2]
life_expectancy_w_dis_n_exp[,3] <- apply(esp_vie_sans_mal_nonconso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_w_dis_n_exp[,4] <- life_expectancy_w_dis_n_exp[,2] - 1.96*life_expectancy_w_dis_n_exp[,3];
life_expectancy_w_dis_n_exp[,5] <- life_expectancy_w_dis_n_exp[,2] + 1.96*life_expectancy_w_dis_n_exp[,3];
for (i in 1:nrow(life_expectancy_w_dis_n_exp)) {
if (life_expectancy_w_dis_n_exp[i,4]<0 & is.na(life_expectancy_w_dis_n_exp[i,4])==F) {
life_expectancy_w_dis_n_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_w_dis_n_exp)) {
if (life_expectancy_w_dis_n_exp[i,2]==0 & life_expectancy_w_dis_n_exp[i,4]==0 & life_expectancy_w_dis_n_exp[i,5]==0 &
is.na(life_expectancy_w_dis_n_exp[i,2])==F & is.na(life_expectancy_w_dis_n_exp[i,4])==F & is.na(life_expectancy_w_dis_n_exp[i,5])==F) {
life_expectancy_w_dis_n_exp[i,2] <- NA;
life_expectancy_w_dis_n_exp[i,3] <- NA;
life_expectancy_w_dis_n_exp[i,4] <- NA;
life_expectancy_w_dis_n_exp[i,5] <- NA
}
}
colnames(life_expectancy_w_dis_n_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for diseased subject
life_expectancy_dis <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_dis[,1] <- esp_vie_mal[,1]
life_expectancy_dis[,2] <- esp_vie_mal[,2]
life_expectancy_dis[,3] <- apply(esp_vie_mal[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_dis[,4] <- life_expectancy_dis[,2] - 1.96*life_expectancy_dis[,3];
life_expectancy_dis[,5] <- life_expectancy_dis[,2] + 1.96*life_expectancy_dis[,3];
for (i in 1:nrow(life_expectancy_dis)) {
if (life_expectancy_dis[i,4]<0 & is.na(life_expectancy_dis[i,4])==F) {
life_expectancy_dis[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_dis)) {
if (life_expectancy_dis[i,2]==0 & life_expectancy_dis[i,4]==0 & life_expectancy_dis[i,5]==0 &
is.na(life_expectancy_dis[i,2])==F & is.na(life_expectancy_dis[i,4])==F & is.na(life_expectancy_dis[i,5])==F) {
life_expectancy_dis[i,2] <- NA;
life_expectancy_dis[i,3] <- NA;
life_expectancy_dis[i,4] <- NA;
life_expectancy_dis[i,5] <- NA
}
}
colnames(life_expectancy_dis) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for diseased subject on exposed peoples
life_expectancy_dis_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_dis_exp[,1] <- esp_vie_mal_conso[,1]
life_expectancy_dis_exp[,2] <- esp_vie_mal_conso[,2]
life_expectancy_dis_exp[,3] <- apply(esp_vie_mal_conso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_dis_exp[,4] <- life_expectancy_dis_exp[,2] - 1.96*life_expectancy_dis_exp[,3];
life_expectancy_dis_exp[,5] <- life_expectancy_dis_exp[,2] + 1.96*life_expectancy_dis_exp[,3];
for (i in 1:nrow(life_expectancy_dis_exp)) {
if (life_expectancy_dis_exp[i,4]<0 & is.na(life_expectancy_dis_exp[i,4])==F) {
life_expectancy_dis_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_dis_exp)) {
if (life_expectancy_dis_exp[i,2]==0 & life_expectancy_dis_exp[i,4]==0 & life_expectancy_dis_exp[i,5]==0 &
is.na(life_expectancy_dis_exp[i,2])==F & is.na(life_expectancy_dis_exp[i,4])==F & is.na(life_expectancy_dis_exp[i,5])==F) {
life_expectancy_dis_exp[i,2] <- NA;
life_expectancy_dis_exp[i,3] <- NA;
life_expectancy_dis_exp[i,4] <- NA;
life_expectancy_dis_exp[i,5] <- NA
}
}
colnames(life_expectancy_dis_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for diseased subject on non exposed peoples
life_expectancy_dis_n_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_dis_n_exp[,1] <- esp_vie_mal_nonconso[,1]
life_expectancy_dis_n_exp[,2] <- esp_vie_mal_nonconso[,2]
life_expectancy_dis_n_exp[,3] <- apply(esp_vie_mal_nonconso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_dis_n_exp[,4] <- life_expectancy_dis_n_exp[,2] - 1.96*life_expectancy_dis_n_exp[,3];
life_expectancy_dis_n_exp[,5] <- life_expectancy_dis_n_exp[,2] + 1.96*life_expectancy_dis_n_exp[,3];
for (i in 1:nrow(life_expectancy_dis_n_exp)) {
if (life_expectancy_dis_n_exp[i,4]<0 & is.na(life_expectancy_dis_n_exp[i,4])==F) {
life_expectancy_dis_n_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_dis_n_exp)) {
if (life_expectancy_dis_n_exp[i,2]==0 & life_expectancy_dis_n_exp[i,4]==0 & life_expectancy_dis_n_exp[i,5]==0 &
is.na(life_expectancy_dis_n_exp[i,2])==F & is.na(life_expectancy_dis_n_exp[i,4])==F & is.na(life_expectancy_dis_n_exp[i,5])==F) {
life_expectancy_dis_n_exp[i,2] <- NA;
life_expectancy_dis_n_exp[i,3] <- NA;
life_expectancy_dis_n_exp[i,4] <- NA;
life_expectancy_dis_n_exp[i,5] <- NA
}
}
colnames(life_expectancy_dis_n_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for non diseased subject
life_expectancy_n_dis <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_n_dis[,1] <- esp_vie_non_mal[,1]
life_expectancy_n_dis[,2] <- esp_vie_non_mal[,2]
life_expectancy_n_dis[,3] <- apply(esp_vie_non_mal[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_n_dis[,4] <- life_expectancy_n_dis[,2] - 1.96*life_expectancy_n_dis[,3];
life_expectancy_n_dis[,5] <- life_expectancy_n_dis[,2] + 1.96*life_expectancy_n_dis[,3];
for (i in 1:nrow(life_expectancy_n_dis)) {
if (life_expectancy_n_dis[i,4]<0 & is.na(life_expectancy_n_dis[i,4])==F) {
life_expectancy_n_dis[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_n_dis)) {
if (life_expectancy_n_dis[i,2]==0 & life_expectancy_n_dis[i,4]==0 & life_expectancy_n_dis[i,5]==0 &
is.na(life_expectancy_n_dis[i,2])==F & is.na(life_expectancy_n_dis[i,4])==F & is.na(life_expectancy_n_dis[i,5])==F) {
life_expectancy_n_dis[i,2] <- NA;
life_expectancy_n_dis[i,3] <- NA;
life_expectancy_n_dis[i,4] <- NA;
life_expectancy_n_dis[i,5] <- NA
}
}
colnames(life_expectancy_n_dis) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for non diseased subject on exposed peoples
life_expectancy_n_dis_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_n_dis_exp[,1] <- esp_vie_non_mal_conso[,1]
life_expectancy_n_dis_exp[,2] <- esp_vie_non_mal_conso[,2]
life_expectancy_n_dis_exp[,3] <- apply(esp_vie_non_mal_conso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_n_dis_exp[,4] <- life_expectancy_n_dis_exp[,2] - 1.96*life_expectancy_n_dis_exp[,3];
life_expectancy_n_dis_exp[,5] <- life_expectancy_n_dis_exp[,2] + 1.96*life_expectancy_n_dis_exp[,3];
for (i in 1:nrow(life_expectancy_n_dis_exp)) {
if (life_expectancy_n_dis_exp[i,4]<0 & is.na(life_expectancy_n_dis_exp[i,4])==F) {
life_expectancy_n_dis_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_n_dis_exp)) {
if (life_expectancy_n_dis_exp[i,2]==0 & life_expectancy_n_dis_exp[i,4]==0 & life_expectancy_n_dis_exp[i,5]==0 &
is.na(life_expectancy_n_dis_exp[i,2])==F & is.na(life_expectancy_n_dis_exp[i,4])==F & is.na(life_expectancy_n_dis_exp[i,5])==F) {
life_expectancy_n_dis_exp[i,2] <- NA;
life_expectancy_n_dis_exp[i,3] <- NA;
life_expectancy_n_dis_exp[i,4] <- NA;
life_expectancy_n_dis_exp[i,5] <- NA
}
}
colnames(life_expectancy_n_dis_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for non diseased subject on non exposed peoples
life_expectancy_n_dis_n_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_n_dis_n_exp[,1] <- esp_vie_non_mal_nonconso[,1]
life_expectancy_n_dis_n_exp[,2] <- esp_vie_non_mal_nonconso[,2]
life_expectancy_n_dis_n_exp[,3] <- apply(esp_vie_non_mal_nonconso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_n_dis_n_exp[,4] <- life_expectancy_n_dis_n_exp[,2] - 1.96*life_expectancy_n_dis_n_exp[,3];
life_expectancy_n_dis_n_exp[,5] <- life_expectancy_n_dis_n_exp[,2] + 1.96*life_expectancy_n_dis_n_exp[,3];
for (i in 1:nrow(life_expectancy_n_dis_n_exp)) {
if (life_expectancy_n_dis_n_exp[i,4]<0 & is.na(life_expectancy_n_dis_n_exp[i,4])==F) {
life_expectancy_n_dis_n_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_n_dis_n_exp)) {
if (life_expectancy_n_dis_n_exp[i,2]==0 & life_expectancy_n_dis_n_exp[i,4]==0 & life_expectancy_n_dis_n_exp[i,5]==0 &
is.na(life_expectancy_n_dis_n_exp[i,2])==F & is.na(life_expectancy_n_dis_n_exp[i,4])==F & is.na(life_expectancy_n_dis_n_exp[i,5])==F) {
life_expectancy_n_dis_n_exp[i,2] <- NA;
life_expectancy_n_dis_n_exp[i,3] <- NA;
life_expectancy_n_dis_n_exp[i,4] <- NA;
life_expectancy_n_dis_n_exp[i,5] <- NA
}
}
colnames(life_expectancy_n_dis_n_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Prevalence of disease
prevalence <- prevalence[-1,]
if (nb_iter != 0) {
prev <- colSums(prevalence[,-1])
} else {
prev <- sum(prevalence[,-1])
}
number_prevalence <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
number_prevalence[1] <- as.integer(t);
number_prevalence[2] <- prev[1];
number_prevalence[3] <- sd(prev[-1], na.rm=T);
number_prevalence[4] <- number_prevalence[2] - 1.96*number_prevalence[3];
number_prevalence[5] <- number_prevalence[2] + 1.96*number_prevalence[3];
if (number_prevalence[4]<0 & is.na(number_prevalence[4])==F) {
number_prevalence[4] <- 0
};
if (number_prevalence[2]==0 & number_prevalence[4]==0 & number_prevalence[5]==0 &
is.na(number_prevalence[2])==F & is.na(number_prevalence[4])==F & is.na(number_prevalence[5])==F) {
number_prevalence[2] <- NA;
number_prevalence[3] <- NA;
number_prevalence[4] <- NA;
number_prevalence[5] <- NA
};
colnames(number_prevalence) <- c("year","prevalence","std","CI95_low","CI95_upp");
number_prevalence <- number_prevalence[,-1];
### Prevalence of disease by age
number_prev_age <- matrix(c(0),
nrow=99-66+1,
ncol=5,
byrow = T);
number_prev_age[,1] <- prevalence[,1]
number_prev_age[,2] <- prevalence[,2]
number_prev_age[,3] <- apply(prevalence[,-c(1:2)], 1, sd, na.rm=T);
number_prev_age[,4] <- number_prev_age[,2] - 1.96*number_prev_age[,3];
number_prev_age[,5] <- number_prev_age[,2] + 1.96*number_prev_age[,3];
for (i in 1:nrow(number_prev_age)) {
if (number_prev_age[i,4]<0 & is.na(number_prev_age[i,4])==F) {
number_prev_age[i,4] <- 0
}
}
for (i in 1:nrow(number_prev_age)) {
if (number_prev_age[i,2]==0 & number_prev_age[i,4]==0 & number_prev_age[i,5]==0 &
is.na(number_prev_age[i,2])==F & is.na(number_prev_age[i,4])==F & is.na(number_prev_age[i,5])==F) {
number_prev_age[i,2] <- NA;
number_prev_age[i,3] <- NA;
number_prev_age[i,4] <- NA;
number_prev_age[i,5] <- NA
}
}
colnames(number_prev_age) <- c("age","prevalence","std","CI95_low","CI95_upp");
### Prevalence rate of disease by age
taux_prevalence <- taux_prevalence[-1,]
prev_rate_disease_age <- matrix(c(0),
nrow=99-66+1,
ncol=5,
byrow = T);
prev_rate_disease_age[,1] <- taux_prevalence[,1]
prev_rate_disease_age[,2] <- taux_prevalence[,2]
prev_rate_disease_age[,3] <- apply(taux_prevalence[,-c(1:2)], 1, sd, na.rm=T);
prev_rate_disease_age[,4] <- prev_rate_disease_age[,2] - 1.96*prev_rate_disease_age[,3];
prev_rate_disease_age[,5] <- prev_rate_disease_age[,2] + 1.96*prev_rate_disease_age[,3];
for (i in 1:nrow(prev_rate_disease_age)) {
if (prev_rate_disease_age[i,4]<0 & is.na(prev_rate_disease_age[i,4])==F) {
prev_rate_disease_age[i,4] <- 0
}
}
for (i in 1:nrow(prev_rate_disease_age)) {
if (prev_rate_disease_age[i,5]>1 & is.na(prev_rate_disease_age[i,5])==F) {
prev_rate_disease_age[i,5] <- 1
}
}
for (i in 1:nrow(prev_rate_disease_age)) {
if (prev_rate_disease_age[i,2]==0 & prev_rate_disease_age[i,4]==0 & prev_rate_disease_age[i,5]==0 &
is.na(prev_rate_disease_age[i,2])==F & is.na(prev_rate_disease_age[i,4])==F & is.na(prev_rate_disease_age[i,5])==F) {
prev_rate_disease_age[i,2] <- NA;
prev_rate_disease_age[i,3] <- NA;
prev_rate_disease_age[i,4] <- NA;
prev_rate_disease_age[i,5] <- NA
}
}
colnames(prev_rate_disease_age) <- c("age","prevalence_rate","std","CI95_low","CI95_upp");
### Survival
if (nb_iter != 0) {
surv <- colSums(survie[,-1])
} else {
surv <- sum(survie[,-1])
}
number_survival <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
number_survival[1] <- as.integer(t);
number_survival[2] <- surv[1]
number_survival[3] <- sd(surv[-1], na.rm=T);
number_survival[4] <- number_survival[2] - 1.96*number_survival[3];
number_survival[5] <- number_survival[2] + 1.96*number_survival[3];
if (number_survival[4]<0 & is.na(number_survival[4])==F) {
number_survival[4] <- 0
};
if (number_survival[2]==0 & number_survival[4]==0 & number_survival[5]==0 &
is.na(number_survival[2])==F & is.na(number_survival[4])==F & is.na(number_survival[5])==F) {
number_survival[2] <- NA;
number_survival[3] <- NA;
number_survival[4] <- NA;
number_survival[5] <- NA
};
colnames(number_survival) <- c("year","survival","std","CI95_low","CI95_upp");
number_survival <- number_survival[,-1];
### Survival by age
number_survival_age <- matrix(c(0),
nrow=99-65+1,
ncol=5,
byrow = T);
number_survival_age[,1] <- survie[,1]
number_survival_age[,2] <- survie[,2]
number_survival_age[,3] <- apply(survie[,-c(1:2)], 1, sd, na.rm=T);
number_survival_age[,4] <- number_survival_age[,2] - 1.96*number_survival_age[,3];
number_survival_age[,5] <- number_survival_age[,2] + 1.96*number_survival_age[,3];
for (i in 1:nrow(number_survival_age)) {
if (number_survival_age[i,4]<0 & is.na(number_survival_age[i,4])==F) {
number_survival_age[i,4] <- 0
}
}
for (i in 1:nrow(number_survival_age)) {
if (number_survival_age[i,2]==0 & number_survival_age[i,4]==0 & number_survival_age[i,5]==0 &
is.na(number_survival_age[i,2])==F & is.na(number_survival_age[i,4])==F & is.na(number_survival_age[i,5])==F) {
number_survival_age[i,2] <- NA;
number_survival_age[i,3] <- NA;
number_survival_age[i,4] <- NA;
number_survival_age[i,5] <- NA
}
}
colnames(number_survival_age) <- c("age","survival","std","CI95_low","CI95_upp");
### Survival rate
taux_survivants <- taux_survivants[-1,]
survival_rate <- matrix(c(0),
nrow=99-66+1,
ncol=5,
byrow = T);
survival_rate[,1] <- taux_survivants[,1]
survival_rate[,2] <- taux_survivants[,2]
survival_rate[,3] <- apply(taux_survivants[,-c(1:2)], 1, sd, na.rm=T);
survival_rate[,4] <- survival_rate[,2] - 1.96*survival_rate[,3];
survival_rate[,5] <- survival_rate[,2] + 1.96*survival_rate[,3];
for (i in 1:nrow(survival_rate)) {
if (survival_rate[i,4]<0 & is.na(survival_rate[i,4])==F) {
survival_rate[i,4] <- 0
}
}
for (i in 1:nrow(survival_rate)) {
if (survival_rate[i,5]>1 & is.na(survival_rate[i,5])==F) {
survival_rate[i,5] <- 1
}
}
for (i in 1:nrow(survival_rate)) {
if (survival_rate[i,2]==0 & survival_rate[i,4]==0 & survival_rate[i,5]==0 &
is.na(survival_rate[i,2])==F & is.na(survival_rate[i,4])==F & is.na(survival_rate[i,5])==F) {
survival_rate[i,2] <- NA;
survival_rate[i,3] <- NA;
survival_rate[i,4] <- NA;
survival_rate[i,5] <- NA
}
}
colnames(survival_rate) <- c("age","survival_rate","std","CI95_low","CI95_upp");
### Global prevalence rate of disease
taux_prev_demence <- matrix(c(0),
nrow=1,
ncol=length(prev)+1,
byrow = T);
taux_prev_demence[1] <- t;
for (i in 2:ncol(taux_prev_demence)) {
taux_prev_demence[1,i] <- prev[i-1] / surv[i-1];
};
prev_rate_disease <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
prev_rate_disease[1] <- as.integer(t);
prev_rate_disease[2] <- taux_prev_demence[,2];
prev_rate_disease[3] <- sd(taux_prev_demence[,-c(1:2)], na.rm=T);
prev_rate_disease[4] <- prev_rate_disease[2] - 1.96*prev_rate_disease[3];
prev_rate_disease[5] <- prev_rate_disease[2] + 1.96*prev_rate_disease[3];
if (prev_rate_disease[4]<0 & is.na(prev_rate_disease[4])==F) {
prev_rate_disease[4] <- 0
};
if (prev_rate_disease[2]==0 & prev_rate_disease[4]==0 & prev_rate_disease[5]==0 &
is.na(prev_rate_disease[2])==F & is.na(prev_rate_disease[4])==F & is.na(prev_rate_disease[5])==F) {
prev_rate_disease[2] <- NA;
prev_rate_disease[3] <- NA;
prev_rate_disease[4] <- NA;
prev_rate_disease[5] <- NA
};
colnames(prev_rate_disease) <- c("year","prevalence_rate","std","CI95_low","CI95_upp");
prev_rate_disease <- prev_rate_disease[,-1];
### Mean number of years spent with disease
number_years_disease <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
number_years_disease[,1] <- nb_moy_dem[,1]
number_years_disease[,2] <- nb_moy_dem[,2]
number_years_disease[,3] <- apply(nb_moy_dem[,-c(1:2)], 1, sd, na.rm=T);
number_years_disease[,4] <- number_years_disease[,2] - 1.96*number_years_disease[,3];
number_years_disease[,5] <- number_years_disease[,2] + 1.96*number_years_disease[,3];
for (i in 1:nrow(number_years_disease)) {
if (number_years_disease[i,4]<0 & is.na(number_years_disease[i,4])==F) {
number_years_disease[i,4] <- 0
}
}
for (i in 1:nrow(number_years_disease)) {
if (number_years_disease[i,2]==0 & number_years_disease[i,4]==0 & number_years_disease[i,5]==0 &
is.na(number_years_disease[i,2])==F & is.na(number_years_disease[i,4])==F & is.na(number_years_disease[i,5])==F) {
number_years_disease[i,2] <- NA;
number_years_disease[i,3] <- NA;
number_years_disease[i,4] <- NA;
number_years_disease[i,5] <- NA
}
}
colnames(number_years_disease) <- c("age","number_years","std","CI95_low","CI95_upp");
### Mean number of years spent with disease on exposed peoples
number_years_disease_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
number_years_disease_exp[,1] <- nb_moy_dem_conso[,1]
number_years_disease_exp[,2] <- nb_moy_dem_conso[,2]
number_years_disease_exp[,3] <- apply(nb_moy_dem_conso[,-c(1:2)], 1, sd, na.rm=T);
number_years_disease_exp[,4] <- number_years_disease_exp[,2] - 1.96*number_years_disease_exp[,3];
number_years_disease_exp[,5] <- number_years_disease_exp[,2] + 1.96*number_years_disease_exp[,3];
for (i in 1:nrow(number_years_disease_exp)) {
if (number_years_disease_exp[i,4]<0 & is.na(number_years_disease_exp[i,4])==F) {
number_years_disease_exp[i,4] <- 0
}
}
for (i in 1:nrow(number_years_disease_exp)) {
if (number_years_disease_exp[i,2]==0 & number_years_disease_exp[i,4]==0 & number_years_disease_exp[i,5]==0 &
is.na(number_years_disease_exp[i,2])==F & is.na(number_years_disease_exp[i,4])==F & is.na(number_years_disease_exp[i,5])==F) {
number_years_disease_exp[i,2] <- NA;
number_years_disease_exp[i,3] <- NA;
number_years_disease_exp[i,4] <- NA;
number_years_disease_exp[i,5] <- NA
}
}
colnames(number_years_disease_exp) <- c("age","number_years","std","CI95_low","CI95_upp");
### Mean number of years spent with disease on non exposed peoples
number_years_disease_n_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
number_years_disease_n_exp[,1] <- nb_moy_dem_nonconso[,1]
number_years_disease_n_exp[,2] <- nb_moy_dem_nonconso[,2]
number_years_disease_n_exp[,3] <- apply(nb_moy_dem_nonconso[,-c(1:2)], 1, sd, na.rm=T);
number_years_disease_n_exp[,4] <- number_years_disease_n_exp[,2] - 1.96*number_years_disease_n_exp[,3];
number_years_disease_n_exp[,5] <- number_years_disease_n_exp[,2] + 1.96*number_years_disease_n_exp[,3];
for (i in 1:nrow(number_years_disease_n_exp)) {
if (number_years_disease_n_exp[i,4]<0 & is.na(number_years_disease_n_exp[i,4])==F) {
number_years_disease_n_exp[i,4] <- 0
}
}
for (i in 1:nrow(number_years_disease_n_exp)) {
if (number_years_disease_n_exp[i,2]==0 & number_years_disease_n_exp[i,4]==0 & number_years_disease_n_exp[i,5]==0 &
is.na(number_years_disease_n_exp[i,2])==F & is.na(number_years_disease_n_exp[i,4])==F & is.na(number_years_disease_n_exp[i,5])==F) {
number_years_disease_n_exp[i,2] <- NA;
number_years_disease_n_exp[i,3] <- NA;
number_years_disease_n_exp[i,4] <- NA;
number_years_disease_n_exp[i,5] <- NA
}
}
colnames(number_years_disease_n_exp) <- c("age","number_years","std","CI95_low","CI95_upp");
### Life-long probability of disease
prb_dem <- prb_dem[-1,]
prb_demence <- matrix(c(0),
nrow=1,
ncol=ncol(prb_dem),
byrow = T);
prb_demence[1] <- t;
for (i in 2:ncol(prb_dem)) {
prb_demence[i] <- sum(prb_dem[,i]) / nrow(etat);
};
ll_prob_disease <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
ll_prob_disease[1] <- as.integer(t);
ll_prob_disease[2] <- prb_demence[,2];
ll_prob_disease[3] <- sd(prb_demence[,-c(1:2)], na.rm=T);
ll_prob_disease[4] <- ll_prob_disease[2] - 1.96*ll_prob_disease[3];
ll_prob_disease[5] <- ll_prob_disease[2] + 1.96*ll_prob_disease[3];
if (ll_prob_disease[4]<0 & is.na(ll_prob_disease[4])==F) {
ll_prob_disease[4] <- 0
};
if (ll_prob_disease[2]==0 & ll_prob_disease[4]==0 & ll_prob_disease[5]==0 &
is.na(ll_prob_disease[2])==F & is.na(ll_prob_disease[4])==F & is.na(ll_prob_disease[5])==F) {
ll_prob_disease[2] <- NA;
ll_prob_disease[3] <- NA;
ll_prob_disease[4] <- NA;
ll_prob_disease[5] <- NA
};
colnames(ll_prob_disease) <- c("year","probability","std","CI95_low","CI95_upp");
ll_prob_disease <- ll_prob_disease[,-1];
### Average age at disease onset
age_dem <- age_dem[-1,]
age_demence <- matrix(c(0),
nrow=1,
ncol=ncol(age_dem),
byrow = T);
age_demence[1] <- t;
n0 <- vector(length = nrow(age_dem));
for (i in 2:ncol(age_dem)) {
for (j in 1:nrow(age_dem)) {
n0[j] <- age_dem[j,1]*age_dem[j,i]
};
age_demence[i] <- sum(n0)/sum(age_dem[,i]);
}
average_age_disease <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
average_age_disease[1] <- as.integer(t);
average_age_disease[2] <- age_demence[,2]
average_age_disease[3] <- sd(age_demence[,-c(1:2)], na.rm=T);
average_age_disease[4] <- average_age_disease[2] - 1.96*average_age_disease[3];
average_age_disease[5] <- average_age_disease[2] + 1.96*average_age_disease[3];
if (average_age_disease[4]<0 & is.na(average_age_disease[4])==F) {
average_age_disease[4] <- 0
};
if (average_age_disease[2]==0 & average_age_disease[4]==0 & average_age_disease[5]==0 &
is.na(average_age_disease[2])==F & is.na(average_age_disease[4])==F & is.na(average_age_disease[5])==F) {
average_age_disease[2] <- NA;
average_age_disease[3] <- NA;
average_age_disease[4] <- NA;
average_age_disease[5] <- NA
};
colnames(average_age_disease) <- c("year","mean_age","std","CI95_low","CI95_upp");
average_age_disease <- average_age_disease[,-1];
### Mean number of years of exposition
moy_conso <- moy_conso[-1,]
moyenne_conso <- matrix(c(0),
nrow=1,
ncol=ncol(moy_conso),
byrow = T);
moyenne_conso[1] <- t;
for (i in 2:ncol(moy_conso)) {
moyenne_conso[i] <- sum(moy_conso[,i]) / nrow(etat[which(etat[,1]%in%("00") | etat[,1]%in%("10")),]); # nombre moyen d'années de consommation de benzo
}
number_years_exp <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
number_years_exp[1] <- as.integer(t);
number_years_exp[2] <- moyenne_conso[,2]
number_years_exp[3] <- sd(moyenne_conso[,-c(1:2)], na.rm=T);
number_years_exp[4] <- number_years_exp[2] - 1.96*number_years_exp[3];
number_years_exp[5] <- number_years_exp[2] + 1.96*number_years_exp[3];
if (number_years_exp[4]<0 & is.na(number_years_exp[4])==F) {
number_years_exp[4] <- 0
};
if (number_years_exp[2]==0 & number_years_exp[4]==0 & number_years_exp[5]==0 &
is.na(number_years_exp[2])==F & is.na(number_years_exp[4])==F & is.na(number_years_exp[5])==F) {
number_years_exp[2] <- NA;
number_years_exp[3] <- NA;
number_years_exp[4] <- NA;
number_years_exp[5] <- NA
};
colnames(number_years_exp) <- c("year","mean_number","std","CI95_low","CI95_upp");
number_years_exp <- number_years_exp[,-1];
### Number of exposed peoples at least one time
exposition <- matrix(c(0),
nrow=105-65+1,
ncol=5,
byrow = T);
exposition[,1] <- prevalence_conso[,1]
exposition[,2] <- prevalence_conso[,2]
exposition[,3] <- apply(prevalence_conso[,-c(1:2)], 1, sd, na.rm=T);
exposition[,4] <- exposition[,2] - 1.96*exposition[,3];
exposition[,5] <- exposition[,2] + 1.96*exposition[,3];
for (i in 1:nrow(exposition)) {
if (exposition[i,4]<0 & is.na(exposition[i,4])==F) {
exposition[i,4] <- 0
}
}
for (i in 1:nrow(exposition)) {
if (exposition[i,5]>1 & is.na(exposition[i,5])==F) {
exposition[i,5] <- 1
}
}
for (i in 1:nrow(exposition)) {
if (exposition[i,2]==0 & exposition[i,4]==0 & exposition[i,5]==0 &
is.na(exposition[i,2])==F & is.na(exposition[i,4])==F & is.na(exposition[i,5])==F) {
exposition[i,2] <- NA;
exposition[i,3] <- NA;
exposition[i,4] <- NA;
exposition[i,5] <- NA
}
}
colnames(exposition) <- c("age","exposition","std","CI95_low","CI95_upp");
### Mortality rate
quotient_mortalite <- quotient_mortalite[-1,]
mortality_rate <- matrix(c(0),
nrow=99-66+1,
ncol=5,
byrow = T);
mortality_rate[,1] <- quotient_mortalite[,1]
mortality_rate[,2] <- quotient_mortalite[,2]
mortality_rate[,3] <- apply(quotient_mortalite[,-c(1:2)], 1, sd, na.rm=T);
mortality_rate[,4] <- mortality_rate[,2] - 1.96*mortality_rate[,3];
mortality_rate[,5] <- mortality_rate[,2] + 1.96*mortality_rate[,3];
for (i in 1:nrow(mortality_rate)) {
if (mortality_rate[i,4]<0 & is.na(mortality_rate[i,4])==F) {
mortality_rate[i,4] <- 0
}
}
for (i in 1:nrow(mortality_rate)) {
if (mortality_rate[i,5]>1 & is.na(mortality_rate[i,5])==F) {
mortality_rate[i,5] <- 1
}
}
for (i in 1:nrow(mortality_rate)) {
if (mortality_rate[i,2]==0 & mortality_rate[i,4]==0 & mortality_rate[i,5]==0 &
is.na(mortality_rate[i,2])==F & is.na(mortality_rate[i,4])==F & is.na(mortality_rate[i,5])==F) {
mortality_rate[i,2] <- NA;
mortality_rate[i,3] <- NA;
mortality_rate[i,4] <- NA;
mortality_rate[i,5] <- NA
}
}
colnames(mortality_rate) <- c("age","mortality_rate","std","CI95_low","CI95_upp");
### Output of the algorithm
### Overall life-expectancy
list_overall_LE <- list(life_expectancy, life_expectancy_exp, life_expectancy_n_exp)
names(list_overall_LE) <- c("life_expectancy", "life_expectancy_exp", "life_expectancy_n_exp")
### Life-expectancy without disease
list_LE_without_disease <- list(life_expectancy_w_dis, life_expectancy_w_dis_exp, life_expectancy_w_dis_n_exp)
names(list_LE_without_disease) <- c("life_expectancy_w_dis", "life_expectancy_w_dis_exp", "life_expectancy_w_dis_n_exp")
### Life-expectancy for diseased subject
list_LE_diseased <- list(life_expectancy_dis, life_expectancy_dis_exp, life_expectancy_dis_n_exp)
names(list_LE_diseased) <- c("life_expectancy_dis", "life_expectancy_dis_exp", "life_expectancy_dis_n_exp")
### Life-expectancy for non-diseased subject
list_LE_non_diseased <- list(life_expectancy_n_dis, life_expectancy_n_dis_exp, life_expectancy_n_dis_n_exp)
names(list_LE_non_diseased) <- c("life_expectancy_n_dis", "life_expectancy_n_dis_exp", "life_expectancy_n_dis_n_exp")
### Prevalence of disease
list_prevalence_disease <- list(number_prev_age, number_prevalence, prev_rate_disease_age, prev_rate_disease)
names(list_prevalence_disease) <- c("number_prev_age", "number_prevalence", "prev_rate_disease_age", "prev_rate_disease")
### Survival
list_survival <- list(number_survival_age, number_survival, survival_rate)
names(list_survival) <- c("number_survival_age", "number_survival", "survival_rate")
### Mean number of years spent with disease
list_number_years_disease <- list(number_years_disease, number_years_disease_exp, number_years_disease_n_exp)
names(list_number_years_disease) <- c("number_years_disease", "number_years_disease_exp", "number_years_disease_n_exp")
### Summary of all iterations
list_summary_iterations <- list(esp_vie_sans_mal, prevalence, taux_prevalence, prb_dem, age_dem, nb_moy_dem, prev, surv)
names(list_summary_iterations) <- c("esp_vie_sans_mal", "prevalence", "taux_prevalence", "prb_dem", "age_dem", "nb_moy_dem", "prev", "surv")
### Output list
# With exposition
#HI_output <- list(list_overall_LE, list_LE_without_disease, list_LE_diseased, list_LE_non_diseased, list_prevalence_disease, list_survival, list_number_years_disease, ll_prob_disease, average_age_disease, number_years_exp, exposition, mortality_rate);
#names(HI_output) <- c(list_overall_LE", "list_LE_without_disease", "list_LE_diseased", "list_LE_non_diseased", "list_prevalence_disease", "list_survival", "list_number_years_disease", "ll_prob_disease", "average_age_disease", "number_years_exp", "exposition", "mortality_rate");
# Without exposition
HI_output <- list(list_overall_LE, list_LE_without_disease, list_LE_diseased, list_LE_non_diseased, list_prevalence_disease, list_survival, list_number_years_disease, list_summary_iterations, ll_prob_disease, average_age_disease, number_years_exp, mortality_rate);
names(HI_output) <- c("list_overall_LE", "list_LE_without_disease", "list_LE_diseased", "list_LE_non_diseased", "list_prevalence_disease", "list_survival", "list_number_years_disease", "list_summary_iterations", "ll_prob_disease", "average_age_disease", "number_years_exp", "mortality_rate");
return(HI_output)
}
floguillet/MCSPCD documentation built on Dec. 16, 2019, 2:08 a.m.
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Defines functions estimHI_3FDR
Documented in estimHI_3FDR
#' Computation of Health Indicators
#'
#' This function computes many health indicators under several scenarios of intervention in risk factor distribution for a given year.
#'
#' @param t year of the projections for health indicators.
#' @param intervention 0 = no change; 1 = risk factor prevalence and incidence considered as null. Default is \code{0}.
#' @param year_intervention year of the intervention in risk factor distribution takes place. Default is \code{NULL}.
#' @param nb_people number of people whose trajectory will be simulated for each generation. Default is \code{100}.
#' @param nb_iter number of iterations for the algorithm. Default is \code{0}.
#' @param data_pop data source for demographics data.
#' @param gender gender for computation. \code{"W"} for women and \code{"M"} for men. Default is \code{"W"}.
#' @param data_a01_values data source for the incidence of disease.
#' @param data_a02_values data source for the mortality of healthy subjects.
#' @param data_theta01_1_values data source for the relative risks associated with the exposure 1 for disease.
#' @param data_theta01_2_values data source for the relative risks associated with the exposure 2 for disease.
#' @param data_theta01_3_values data source for the relative risks associated with the exposure 3 for disease.
#' @param data_theta02_1_values data source for the relative risks associated with the exposure 1 for mortality among healthy subjects.
#' @param data_theta02_2_values data source for the relative risks associated with the exposure 2 for mortality among healthy subjects.
#' @param data_theta02_3_values data source for the relative risks associated with the exposure 3 for mortality among healthy subjects.
#' @param data_theta12_1_values data source for the relative risks associated with the exposure 1 for mortality among diseased subjects.
#' @param data_theta12_2_values data source for the relative risks associated with the exposure 2 for mortality among diseased subjects.
#' @param data_theta12_3_values data source for the relative risks associated with the exposure 3 for mortality among diseased subjects.
#' @param data_prev_0_values data source for the prevalence of the exposition 0.
#' @param data_prev_1_values data source for the prevalence of the exposition 1.
#' @param data_prev_2_values data source for the prevalence of the exposition 2.
#' @param data_prev_3_values data source for the prevalence of the exposition 3.
#' @param data_prev_4_values data source for the prevalence of the exposition 4.
#' @param data_prev_5_values data source for the prevalence of the exposition 5.
#' @param data_prev_6_values data source for the prevalence of the exposition 6.
#' @param data_prev_7_values data source for the prevalence of the exposition 7.
#' @param data_incid_0_values data source for the incidence of the exposition 0.
#' @param data_incid_1_values data source for the incidence of the exposition 1.
#' @param data_incid_3_values data source for the incidence of the exposition 3.
#' @param data_incid_5_values data source for the incidence of the exposition 5.
#' @param data_rr_DvsND_values data source for the relative risks associated with the disease for mortality.
#' @param data_a01 variability of data source for the incidence of disease.
#' @param data_a02 variability of data source for the mortality of healthy subjects.
#' @param data_theta01_1 variability of data source for the relative risks associated with the exposure 1 for disease.
#' @param data_theta01_2 variability of data source for the relative risks associated with the exposure 2 for disease.
#' @param data_theta01_3 variability of data source for the relative risks associated with the exposure 3 for disease.
#' @param data_theta02_1 variability of data source for the relative risks associated with the exposure 1 for mortality among healthy subjects.
#' @param data_theta02_2 variability of data source for the relative risks associated with the exposure 2 for mortality among healthy subjects.
#' @param data_theta02_3 variability of data source for the relative risks associated with the exposure 3 for mortality among healthy subjects.
#' @param data_theta12_1 variability of data source for the relative risks associated with the exposure 1 for mortality among diseased subjects.
#' @param data_theta12_2 variability of data source for the relative risks associated with the exposure 2 for mortality among diseased subjects.
#' @param data_theta12_3 variability of data source for the relative risks associated with the exposure 3 for mortality among diseased subjects.
#' @param data_prev_0 variability of data source for the prevalence of the exposition 0.
#' @param data_prev_1 variability of data source for the prevalence of the exposition 1.
#' @param data_prev_2 variability of data source for the prevalence of the exposition 2.
#' @param data_prev_3 variability of data source for the prevalence of the exposition 3.
#' @param data_prev_4 variability of data source for the prevalence of the exposition 4.
#' @param data_prev_5 variability of data source for the prevalence of the exposition 5.
#' @param data_prev_6 variability of data source for the prevalence of the exposition 6.
#' @param data_prev_7 variability of data source for the prevalence of the exposition 7.
#' @param data_incid_0 variability of data source for the incidence of the exposition 0.
#' @param data_incid_1 variability of data source for the incidence of the exposition 1.
#' @param data_incid_3 variability of data source for the incidence of the exposition 3.
#' @param data_incid_5 variability of data source for the incidence of the exposition 5.
#' @param data_rr_DvsND variability of data source for the relative risks associated with the disease for mortality.
#' @param Ncpus The number of processors available. Default is \code{"1"}.
#'
#' @return a list containing the health indicators
#'
#' @export
#'
#' @examples
#' estimHI_3FDR(t = 2040,
#' intervention = 1,
#' year_intervention = 2020,
#' nb_people = 10000,
#' nb_iter = 100,
#' data_pop = pop,
#' gender = "W",
#' data_a01_values = a01_constant_values,
#' data_a02_values = a02_constant_values,
#' data_theta01_1_values = theta01_cas_1_6_values,
#' data_theta01_2_values = theta01_cas_1_6_values,
#' data_theta01_3_values = theta01_cas_1_6_values,
#' data_theta02_1_values = theta02_increase_values,
#' data_theta02_2_values = theta02_increase_values,
#' data_theta02_3_values = theta02_increase_values,
#' data_theta12_1_values = theta02_increase_values,
#' data_theta12_2_values = theta02_increase_values,
#' data_theta12_3_values = theta02_increase_values,
#' data_prev_0_values <- prevconso_values,
#' data_prev_1_values <- prevconso_values,
#' data_prev_2_values <- prevconso_values,
#' data_prev_3_values <- prevconso_values,
#' data_prev_4_values <- prevconso_values,
#' data_prev_5_values <- prevconso_values,
#' data_prev_6_values <- prevconso_values,
#' data_prev_7_values <- prevconso_values,
#' data_incid_0_values <- incidconso_values,
#' data_incid_1_values <- incidconso_values,
#' data_incid_3_values <- incidconso_values,
#' data_incid_5_values <- incidconso_values,
#' data_rr_DvsND_values = rr_DvsND_values,
#' data_a01 = a01_constant,
#' data_a02 = a02_constant,
#' data_theta01_1 = theta01_cas_1_6,
#' data_theta01_2 = theta01_cas_1_6,
#' data_theta01_3 = theta01_cas_1_6,
#' data_theta02_1 = theta02_increase,
#' data_theta02_2 = theta02_increase,
#' data_theta02_3 = theta02_increase,
#' data_theta12_1 = theta02_increase,
#' data_theta12_2 = theta02_increase,
#' data_theta12_3 = theta02_increase,
#' data_prev_0 = prevconso,
#' data_prev_1 = prevconso,
#' data_prev_2 = prevconso,
#' data_prev_3 = prevconso,
#' data_prev_4 = prevconso,
#' data_prev_5 = prevconso,
#' data_prev_6 = prevconso,
#' data_prev_7 = prevconso,
#' data_incid_0 = incidconso,
#' data_incid_1 = incidconso,
#' data_incid_3 = incidconso,
#' data_incid_5 = incidconso,
#' data_rr_DvsND = rr_DvsND,
#' Ncpus = 1)
estimHI_3FDR <- function(t,
intervention = 0,
year_intervention = NULL,
nb_people = 100,
nb_iter = 0,
data_pop,
gender = "W",
data_a01_values,
data_a02_values,
data_theta01_1_values,
data_theta01_2_values,
data_theta01_3_values,
data_theta02_1_values,
data_theta02_2_values,
data_theta02_3_values,
data_theta12_1_values,
data_theta12_2_values,
data_theta12_3_values,
data_prev_0_values,
data_prev_1_values,
data_prev_2_values,
data_prev_3_values,
data_prev_4_values,
data_prev_5_values,
data_prev_6_values,
data_prev_7_values,
data_incid_0_values,
data_incid_1_values,
data_incid_3_values,
data_incid_5_values,
data_rr_DvsND_values,
data_a01,
data_a02,
data_theta01_1,
data_theta01_2,
data_theta01_3,
data_theta02_1,
data_theta02_2,
data_theta02_3,
data_theta12_1,
data_theta12_2,
data_theta12_3,
data_prev_0,
data_prev_1,
data_prev_2,
data_prev_3,
data_prev_4,
data_prev_5,
data_prev_6,
data_prev_7,
data_incid_0,
data_incid_1,
data_incid_3,
data_incid_5,
data_rr_DvsND,
Ncpus = 1)
{
### Year of projection
set.seed(0)
year_proj <- t
### Incidence of disease on non exposed peoples
a010 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a010) <- c("age",1950:2080)
a010[,1] <- c(66:105)
for (a in 2:ncol(a010)){
a010[,a] <- as.numeric(data_a01[which(data_a01[,1] != 65 & data_a01[,2]%in%(gender)),a+1]) / (data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2] + data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2] + data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2] + data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2]*data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2] + data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2] + data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2]*data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2] + data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2]*data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2] + data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2]*data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]);
}
### Incidence of disease on exposed peoples
a011 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a011) <- c("age",1950:2080)
a011[,1] <- c(66:105)
a011[,-1] <- a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2];
a012 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a012) <- c("age",1950:2080)
a012[,1] <- c(66:105)
a012[,-1] <- a010[,-1]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2];
a013 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a013) <- c("age",1950:2080)
a013[,1] <- c(66:105)
a013[,-1] <- a010[,-1]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2];
a014 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a014) <- c("age",1950:2080)
a014[,1] <- c(66:105)
a014[,-1] <- a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2];
a015 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a015) <- c("age",1950:2080)
a015[,1] <- c(66:105)
a015[,-1] <- a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2];
a016 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a016) <- c("age",1950:2080)
a016[,1] <- c(66:105)
a016[,-1] <- a010[,-1]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2];
a017 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a017) <- c("age",1950:2080)
a017[,1] <- c(66:105)
a017[,-1] <- a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2];
### Global incidence of disease
a01_global <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a01_global) <- c("age",1950:2080)
a01_global[,1] <- c(66:105)
a01_global[,-1] <- data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2]*a010[,-1] + data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2]*a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2] + data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2]*a010[,-1]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2] + data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2]*a010[,-1]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2] + data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2]*a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2] + data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2]*a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2] + data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2]*a010[,-1]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2] + data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]*a010[,-1]*data_theta01_1[which(data_theta01_1[,1] != 65 & data_theta01_1[,3]%in%(gender)),2]*data_theta01_2[which(data_theta01_2[,1] != 65 & data_theta01_2[,3]%in%(gender)),2]*data_theta01_3[which(data_theta01_3[,1] != 65 & data_theta01_3[,3]%in%(gender)),2];
### Mortality of healthy subjects on non exposed peoples
a020 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a020) <- c("age",1950:2080)
a020[,1] <- c(66:105)
for (a in 2:ncol(a020)){
a020[,a] <- as.numeric(data_a02[which(data_a02[,1] != 65 & data_a02[,2]%in%(gender)),a+1]) / (data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2] + data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2] + data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2] + data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2]*data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2] + data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2] + data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2]*data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2] + data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2]*data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2] + data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2]*data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]);
}
### Mortality of healthy subjects on exposed peoples
a021 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a021) <- c("age",1950:2080)
a021[,1] <- c(66:105)
a021[,-1] <- a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2];
a022 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a022) <- c("age",1950:2080)
a022[,1] <- c(66:105)
a022[,-1] <- a020[,-1]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2];
a023 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a023) <- c("age",1950:2080)
a023[,1] <- c(66:105)
a023[,-1] <- a020[,-1]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2];
a024 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a024) <- c("age",1950:2080)
a024[,1] <- c(66:105)
a024[,-1] <- a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2];
a025 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a025) <- c("age",1950:2080)
a025[,1] <- c(66:105)
a025[,-1] <- a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2];
a026 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a026) <- c("age",1950:2080)
a026[,1] <- c(66:105)
a026[,-1] <- a020[,-1]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2];
a027 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a027) <- c("age",1950:2080)
a027[,1] <- c(66:105)
a027[,-1] <- a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2];
### Global mortality of healthy subjects
a02_global <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a02_global) <- c("age",1950:2080)
a02_global[,1] <- c(66:105)
a02_global[,-1] <- data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2]*a020[,-1] + data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2]*a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2] + data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2]*a020[,-1]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2] + data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2]*a020[,-1]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2] + data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2]*a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2] + data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2]*a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2] + data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2]*a020[,-1]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2] + data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]*a020[,-1]*data_theta02_1[which(data_theta02_1[,1] != 65 & data_theta02_1[,3]%in%(gender)),2]*data_theta02_2[which(data_theta02_2[,1] != 65 & data_theta02_2[,3]%in%(gender)),2]*data_theta02_3[which(data_theta02_3[,1] != 65 & data_theta02_3[,3]%in%(gender)),2];
### Relative risks associated with the disease for mortality
RR <- matrix(c(0),
nrow=40*nb_iter,
ncol=2,
byrow=T);
colnames(RR) <- c("age","rr_DvsND")
RR[,1] <- c(66:105)
RR[,2] <- data_rr_DvsND[which(data_rr_DvsND[,1] != 65 & data_rr_DvsND[,3]%in%(gender)),2];
### Mortality of diseased subjects on non exposed peoples
a120 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a120) <- c("age",1950:2080)
a120[,1] <- c(66:105)
for (a in 2:ncol(a120)){
a120[,a] <- as.numeric(RR[,2])*as.numeric(data_a02[which(data_a02[,1] != 65 & data_a02[,2]%in%(gender)),a+1]) / (data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2] + data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2] + data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2] + data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2]*data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2] + data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2] + data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2]*data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2] + data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2]*data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2] + data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2]*data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]);
}
### Mortality of diseased subjects on exposed peoples
a121 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a121) <- c("age",1950:2080)
a121[,1] <- c(66:105)
a121[,-1] <- a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2];
a122 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a122) <- c("age",1950:2080)
a122[,1] <- c(66:105)
a122[,-1] <- a120[,-1]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2];
a123 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a123) <- c("age",1950:2080)
a123[,1] <- c(66:105)
a123[,-1] <- a120[,-1]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2];
a124 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a124) <- c("age",1950:2080)
a124[,1] <- c(66:105)
a124[,-1] <- a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2];
a125 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a125) <- c("age",1950:2080)
a125[,1] <- c(66:105)
a125[,-1] <- a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2];
a126 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a126) <- c("age",1950:2080)
a126[,1] <- c(66:105)
a126[,-1] <- a120[,-1]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2];
a127 <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a127) <- c("age",1950:2080)
a127[,1] <- c(66:105)
a127[,-1] <- a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2];
### Global mortality of diseased subjects
a12_global <- matrix(c(0),
nrow=40*nb_iter,
ncol=132,
byrow=T);
colnames(a12_global) <- c("age",1950:2080)
a12_global[,1] <- c(66:105)
a12_global[,-1] <- data_prev_0[which(data_prev_0[,1] != 65 & data_prev_0[,3]%in%(gender)),2]*a120[,-1] + data_prev_1[which(data_prev_1[,1] != 65 & data_prev_1[,3]%in%(gender)),2]*a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2] + data_prev_2[which(data_prev_2[,1] != 65 & data_prev_2[,3]%in%(gender)),2]*a120[,-1]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2] + data_prev_3[which(data_prev_3[,1] != 65 & data_prev_3[,3]%in%(gender)),2]*a120[,-1]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2] + data_prev_4[which(data_prev_4[,1] != 65 & data_prev_4[,3]%in%(gender)),2]*a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2] + data_prev_5[which(data_prev_5[,1] != 65 & data_prev_5[,3]%in%(gender)),2]*a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2] + data_prev_6[which(data_prev_6[,1] != 65 & data_prev_6[,3]%in%(gender)),2]*a120[,-1]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2] + data_prev_7[which(data_prev_7[,1] != 65 & data_prev_7[,3]%in%(gender)),2]*a120[,-1]*data_theta12_1[which(data_theta12_1[,1] != 65 & data_theta12_1[,3]%in%(gender)),2]*data_theta12_2[which(data_theta12_2[,1] != 65 & data_theta12_2[,3]%in%(gender)),2]*data_theta12_3[which(data_theta12_3[,1] != 65 & data_theta12_3[,3]%in%(gender)),2];
### Variability of parameters
### Incidence of disease on non exposed peoples
a010_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a010_values) <- c("age",1950:2080)
a010_values[,1] <- c(66:105)
for (a in 2:ncol(a010_values)){
a010_values[,a] <- as.numeric(data_a01_values[which(data_a01_values[,1] != 65 & data_a01_values[,2]%in%(gender)),a+1]) / (data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2] + data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2] + data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2] + data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]);
}
### Incidence of disease on exposed peoples
a011_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a011_values) <- c("age",1950:2080)
a011_values[,1] <- c(66:105)
a011_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2];
a012_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a012_values) <- c("age",1950:2080)
a012_values[,1] <- c(66:105)
a012_values[,-1] <- a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2];
a013_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a013_values) <- c("age",1950:2080)
a013_values[,1] <- c(66:105)
a013_values[,-1] <- a010_values[,-1]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a014_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a014_values) <- c("age",1950:2080)
a014_values[,1] <- c(66:105)
a014_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2];
a015_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a015_values) <- c("age",1950:2080)
a015_values[,1] <- c(66:105)
a015_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a016_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a016_values) <- c("age",1950:2080)
a016_values[,1] <- c(66:105)
a016_values[,-1] <- a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a017_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a017_values) <- c("age",1950:2080)
a017_values[,1] <- c(66:105)
a017_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
### Global incidence of disease
a01_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a01_global_values) <- c("age",1950:2080)
a01_global_values[,1] <- c(66:105)
a01_global_values[,-1] <- data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2]*a010_values[,-1] + data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2] + data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2] + data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2] + data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
### Mortality of healthy subjects on non exposed peoples
a020_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a020_values) <- c("age",1950:2080)
a020_values[,1] <- c(66:105)
for (a in 2:ncol(a020_values)){
a020_values[,a] <- as.numeric(data_a02_values[which(data_a02_values[,1] != 65 & data_a02_values[,2]%in%(gender)),a+1]) / (data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2] + data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2] + data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2] + data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]);
}
### Mortality of healthy subjects on exposed peoples
a021_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a021_values) <- c("age",1950:2080)
a021_values[,1] <- c(66:105)
a021_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2];
a022_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a022_values) <- c("age",1950:2080)
a022_values[,1] <- c(66:105)
a022_values[,-1] <- a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2];
a023_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a023_values) <- c("age",1950:2080)
a023_values[,1] <- c(66:105)
a023_values[,-1] <- a020_values[,-1]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a024_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a024_values) <- c("age",1950:2080)
a024_values[,1] <- c(66:105)
a024_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2];
a025_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a025_values) <- c("age",1950:2080)
a025_values[,1] <- c(66:105)
a025_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a026_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a026_values) <- c("age",1950:2080)
a026_values[,1] <- c(66:105)
a026_values[,-1] <- a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a027_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a027_values) <- c("age",1950:2080)
a027_values[,1] <- c(66:105)
a027_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
### Global mortality of healthy subjects
a02_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a02_global_values) <- c("age",1950:2080)
a02_global_values[,1] <- c(66:105)
a02_global_values[,-1] <- data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2]*a020_values[,-1] + data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2] + data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2] + data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2] + data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
### Relative risks associated with the disease for mortality
RR_values <- matrix(c(0),
nrow=40,
ncol=2,
byrow=T);
colnames(RR_values) <- c("age","rr_DvsND")
RR_values[,1] <- c(66:105)
RR_values[,2] <- data_rr_DvsND_values[which(data_rr_DvsND_values[,1] != 65 & data_rr_DvsND_values[,3]%in%(gender)),2];
### Mortality of diseased subjects on non exposed peoples
a120_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a120_values) <- c("age",1950:2080)
a120_values[,1] <- c(66:105)
for (a in 2:ncol(a120_values)){
a120_values[,a] <- as.numeric(RR_values[,2])*as.numeric(data_a02_values[which(data_a02_values[,1] != 65 & data_a02_values[,2]%in%(gender)),a+1]) / (data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2] + data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2] + data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2] + data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]);
}
### Mortality of diseased subjects on exposed peoples
a121_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a121_values) <- c("age",1950:2080)
a121_values[,1] <- c(66:105)
a121_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2];
a122_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a122_values) <- c("age",1950:2080)
a122_values[,1] <- c(66:105)
a122_values[,-1] <- a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2];
a123_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a123_values) <- c("age",1950:2080)
a123_values[,1] <- c(66:105)
a123_values[,-1] <- a120_values[,-1]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a124_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a124_values) <- c("age",1950:2080)
a124_values[,1] <- c(66:105)
a124_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2];
a125_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a125_values) <- c("age",1950:2080)
a125_values[,1] <- c(66:105)
a125_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a126_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a126_values) <- c("age",1950:2080)
a126_values[,1] <- c(66:105)
a126_values[,-1] <- a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a127_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a127_values) <- c("age",1950:2080)
a127_values[,1] <- c(66:105)
a127_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
### Global mortality of diseased subjects
a12_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a12_global_values) <- c("age",1950:2080)
a12_global_values[,1] <- c(66:105)
a12_global_values[,-1] <- data_prev_0_values[which(data_prev_0_values[,1] != 65 & data_prev_0_values[,3]%in%(gender)),2]*a120_values[,-1] + data_prev_1_values[which(data_prev_1_values[,1] != 65 & data_prev_1_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2] + data_prev_2_values[which(data_prev_2_values[,1] != 65 & data_prev_2_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2] + data_prev_3_values[which(data_prev_3_values[,1] != 65 & data_prev_3_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_4_values[which(data_prev_4_values[,1] != 65 & data_prev_4_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2] + data_prev_5_values[which(data_prev_5_values[,1] != 65 & data_prev_5_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_6_values[which(data_prev_6_values[,1] != 65 & data_prev_6_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_7_values[which(data_prev_7_values[,1] != 65 & data_prev_7_values[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
### Matrix for results of health indicators
### Overall life-expectancy
esp_vie_gen <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_gen[,1] <- c(65:100);
### Overall life-expectancy on exposed peoples
esp_vie_gen_conso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_gen_conso[,1] <- c(65:100);
### Overall life-expectancy on non exposed peoples
esp_vie_gen_nonconso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_gen_nonconso[,1] <- c(65:100);
### Life-expectancy without disease
esp_vie_sans_mal <- matrix(c(0), # matrice de calcul des espérances de vie à chaque âge
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_sans_mal[,1] <- c(65:100);
### Life-expectancy without disease on exposed peoples
esp_vie_sans_mal_conso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_sans_mal_conso[,1] <- c(65:100);
### Life-expectancy without disease on non exposed peoples
esp_vie_sans_mal_nonconso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_sans_mal_nonconso[,1] <- c(65:100);
### Life-expectancy for diseased subject
esp_vie_mal <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_mal[,1] <- c(65:100);
### Life-expectancy for diseased subject on exposed peoples
esp_vie_mal_conso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_mal_conso[,1] <- c(65:100);
### Life-expectancy for diseased subject on non exposed peoples
esp_vie_mal_nonconso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_mal_nonconso[,1] <- c(65:100);
### Life-expectancy for non-diseased subject
esp_vie_non_mal <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_non_mal[,1] <- c(65:100);
### Life-expectancy for non-diseased subject on exposed peoples
esp_vie_non_mal_conso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_non_mal_conso[,1] <- c(65:100);
### Life-expectancy for non-diseased subject on non exposed peoples
esp_vie_non_mal_nonconso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
esp_vie_non_mal_nonconso[,1] <- c(65:100);
### Number of exposed peoples at least one time
prevalence <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
prevalence[,1] <- c(65:99);
### Rate of exposed peoples at least one time
taux_prevalence <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
taux_prevalence[,1] <- c(65:99);
### Number of living peoples
survie <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
survie[,1] <- c(65:99);
### Rate of living peoples a given
taux_survivants <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
taux_survivants[,1] <- c(65:99);
### Mean number of years spent with disease
nb_moy_dem <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
nb_moy_dem[,1] <- c(65:100);
### Mean number of years spent with disease on exposed peoples
nb_moy_dem_conso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
nb_moy_dem_conso[,1] <- c(65:100);
### Mean number of years spent with disease on non exposed peoples
nb_moy_dem_nonconso <- matrix(c(0),
nrow=100-65+1,
ncol=2+nb_iter,
byrow = T);
nb_moy_dem_nonconso[,1] <- c(65:100);
### Life-long probability of disease
prb_dem <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
prb_dem[,1] <- c(65:99);
### Average age at disease onset
age_dem <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
age_dem[,1] <- c(65:99);
### Mean number of years of exposition
moy_conso <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
moy_conso[,1] <- c(65:99);
### Number of exposed peoples at least one time
prevalence_conso <- matrix(c(0),
nrow=105-65+1,
ncol=2+nb_iter,
byrow=T);
prevalence_conso[,1] <- c(65:105);
### Mortality rate
quotient_mortalite <- matrix(c(0),
nrow=99-65+1,
ncol=2+nb_iter,
byrow=T);
quotient_mortalite[,1] <- c(65:99);
##############################
########## ESSAIS ############
##############################
### Dementia incidence
incid_demence <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
incid_demence[,1] <- c(66:105);
### Mortality incidence
incid_mort_Dem <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
incid_mort_Dem[,1] <- c(66:105);
incid_mort_NoD <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
incid_mort_NoD[,1] <- c(66:105);
### Non exposed prevalence
prevalence_noe <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_noe[,1] <- c(65:105);
### Physical inactivity prevalence
prevalence_ina <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_ina[,1] <- c(65:105);
### Hypertension prevalence
prevalence_hta <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_hta[,1] <- c(65:105);
### Diabete prevalence
prevalence_dia <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_dia[,1] <- c(65:105);
### Expositions prevalence
prevalence_exp0 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp0[,1] <- c(65:105);
prevalence_exp1 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp1[,1] <- c(65:105);
prevalence_exp2 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp2[,1] <- c(65:105);
prevalence_exp3 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp3[,1] <- c(65:105);
prevalence_exp4 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp4[,1] <- c(65:105);
prevalence_exp5 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp5[,1] <- c(65:105);
prevalence_exp6 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp6[,1] <- c(65:105);
prevalence_exp7 <- matrix(c(0),
nrow=105-65+1,
ncol=105-65+2,
byrow=T);
prevalence_exp7[,1] <- c(65:105);
prop_dem_diabet <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
prop_dem_diabet[,1] <- c(66:105);
prop_dem_hypert <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
prop_dem_hypert[,1] <- c(66:105);
prop_dem_inacti <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
prop_dem_inacti[,1] <- c(66:105);
prop_dem_global <- matrix(c(0),
nrow=105-66+1,
ncol=105-65+2,
byrow=T);
prop_dem_global[,1] <- c(66:105);
#####################################################################################
#####################################################################################
#####################################################################################
#####################################################################################
#####################################################################################
### Computation with estimated parameters
for (age in 65:105) { # for each generation
an_naiss <- year_proj-age; # year of birth
an0 <- an_naiss + 65; # first year at risk
annee <- an0; # year of estimation
data_prev_0_values_ND <- data_prev_0_values
data_prev_0_values_D <- data_prev_0_values
data_prev_1_values_ND <- data_prev_1_values
data_prev_1_values_D <- data_prev_1_values
data_prev_2_values_ND <- data_prev_2_values
data_prev_2_values_D <- data_prev_2_values
data_prev_3_values_ND <- data_prev_3_values
data_prev_3_values_D <- data_prev_3_values
data_prev_4_values_ND <- data_prev_4_values
data_prev_4_values_D <- data_prev_4_values
data_prev_5_values_ND <- data_prev_5_values
data_prev_5_values_D <- data_prev_5_values
data_prev_6_values_ND <- data_prev_6_values
data_prev_6_values_D <- data_prev_6_values
data_prev_7_values_ND <- data_prev_7_values
data_prev_7_values_D <- data_prev_7_values
a010_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a010_values) <- c("age",1950:2080)
a010_values[,1] <- c(66:105)
a011_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a011_values) <- c("age",1950:2080)
a011_values[,1] <- c(66:105)
a012_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a012_values) <- c("age",1950:2080)
a012_values[,1] <- c(66:105)
a013_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a013_values) <- c("age",1950:2080)
a013_values[,1] <- c(66:105)
a014_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a014_values) <- c("age",1950:2080)
a014_values[,1] <- c(66:105)
a015_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a015_values) <- c("age",1950:2080)
a015_values[,1] <- c(66:105)
a016_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a016_values) <- c("age",1950:2080)
a016_values[,1] <- c(66:105)
a017_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a017_values) <- c("age",1950:2080)
a017_values[,1] <- c(66:105)
a01_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a01_global_values) <- c("age",1950:2080)
a01_global_values[,1] <- c(66:105)
a020_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a020_values) <- c("age",1950:2080)
a020_values[,1] <- c(66:105)
a021_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a021_values) <- c("age",1950:2080)
a021_values[,1] <- c(66:105)
a022_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a022_values) <- c("age",1950:2080)
a022_values[,1] <- c(66:105)
a023_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a023_values) <- c("age",1950:2080)
a023_values[,1] <- c(66:105)
a024_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a024_values) <- c("age",1950:2080)
a024_values[,1] <- c(66:105)
a025_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a025_values) <- c("age",1950:2080)
a025_values[,1] <- c(66:105)
a026_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a026_values) <- c("age",1950:2080)
a026_values[,1] <- c(66:105)
a027_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a027_values) <- c("age",1950:2080)
a027_values[,1] <- c(66:105)
a02_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a02_global_values) <- c("age",1950:2080)
a02_global_values[,1] <- c(66:105)
a120_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a120_values) <- c("age",1950:2080)
a120_values[,1] <- c(66:105)
a121_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a121_values) <- c("age",1950:2080)
a121_values[,1] <- c(66:105)
a122_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a122_values) <- c("age",1950:2080)
a122_values[,1] <- c(66:105)
a123_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a123_values) <- c("age",1950:2080)
a123_values[,1] <- c(66:105)
a124_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a124_values) <- c("age",1950:2080)
a124_values[,1] <- c(66:105)
a125_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a125_values) <- c("age",1950:2080)
a125_values[,1] <- c(66:105)
a126_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a126_values) <- c("age",1950:2080)
a126_values[,1] <- c(66:105)
a127_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a127_values) <- c("age",1950:2080)
a127_values[,1] <- c(66:105)
a12_global_values <- matrix(c(0),
nrow=40,
ncol=132,
byrow=T);
colnames(a12_global_values) <- c("age",1950:2080)
a12_global_values[,1] <- c(66:105)
RR_values <- matrix(c(0),
nrow=40,
ncol=2,
byrow=T);
colnames(RR_values) <- c("age","rr_DvsND")
RR_values[,1] <- c(66:105)
etat <- matrix(c(0), # initial state (non diseased)
nrow=nb_people,
ncol=105-65+1,
byrow=T);
colnames(etat) <- c(65:105);
# RUN 1
for (i in 1:nrow(etat)) {
alea0 <- runif(1, 0, 1);
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]) {
etat[i,1] <- "00" # non diseased and non exposed
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]) {
etat[i,1] <- "01" # non diseased and exposed 1
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]+data_prev_2_values[which(data_prev_2_values[,1]%in%(65) & data_prev_2_values[,3]%in%(gender)),2]) {
etat[i,1] <- "02" # non diseased and exposed 2
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]+data_prev_2_values[which(data_prev_2_values[,1]%in%(65) & data_prev_2_values[,3]%in%(gender)),2]+data_prev_3_values[which(data_prev_3_values[,1]%in%(65) & data_prev_3_values[,3]%in%(gender)),2]) {
etat[i,1] <- "03" # non diseased and exposed 3
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]+data_prev_2_values[which(data_prev_2_values[,1]%in%(65) & data_prev_2_values[,3]%in%(gender)),2]+data_prev_3_values[which(data_prev_3_values[,1]%in%(65) & data_prev_3_values[,3]%in%(gender)),2]+data_prev_4_values[which(data_prev_4_values[,1]%in%(65) & data_prev_4_values[,3]%in%(gender)),2]) {
etat[i,1] <- "04" # non diseased and exposed 4
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]+data_prev_2_values[which(data_prev_2_values[,1]%in%(65) & data_prev_2_values[,3]%in%(gender)),2]+data_prev_3_values[which(data_prev_3_values[,1]%in%(65) & data_prev_3_values[,3]%in%(gender)),2]+data_prev_4_values[which(data_prev_4_values[,1]%in%(65) & data_prev_4_values[,3]%in%(gender)),2]+data_prev_5_values[which(data_prev_5_values[,1]%in%(65) & data_prev_5_values[,3]%in%(gender)),2]) {
etat[i,1] <- "05" # non diseased and exposed 5
} else {
if (alea0 <= data_prev_0_values[which(data_prev_0_values[,1]%in%(65) & data_prev_0_values[,3]%in%(gender)),2]+data_prev_1_values[which(data_prev_1_values[,1]%in%(65) & data_prev_1_values[,3]%in%(gender)),2]+data_prev_2_values[which(data_prev_2_values[,1]%in%(65) & data_prev_2_values[,3]%in%(gender)),2]+data_prev_3_values[which(data_prev_3_values[,1]%in%(65) & data_prev_3_values[,3]%in%(gender)),2]+data_prev_4_values[which(data_prev_4_values[,1]%in%(65) & data_prev_4_values[,3]%in%(gender)),2]+data_prev_5_values[which(data_prev_5_values[,1]%in%(65) & data_prev_5_values[,3]%in%(gender)),2]+data_prev_6_values[which(data_prev_6_values[,1]%in%(65) & data_prev_6_values[,3]%in%(gender)),2]) {
etat[i,1] <- "06" # non diseased and exposed 6
} else {
etat[i,1] <- "07" # non diseased and exposed 7
}
}
}
}
}
}
}
};
for (j in 2:ncol(etat)) { # for each people
for (i in 1:nrow(etat)) { # for each age
alea <- runif(1, 0, 1);
alea0 <- runif(1, 0, 1);
if (etat[i,j-1] == "00") {
if (alea0 <= data_incid_0_values[which(data_incid_0_values[,1]%in%(j-1+65) & data_incid_0_values[,3]%in%(gender)),2]) {
etat[i,j] <- "02"; # non diseased and exposed 2
} else {
etat[i,j] <- "00"; # non diseased and non exposed
}
} else {
if (etat[i,j-1] == "01") {
if (alea0 <= data_incid_1_values[which(data_incid_1_values[,1]%in%(j-1+65) & data_incid_1_values[,3]%in%(gender)),2]) {
etat[i,j] <- "04"; # non diseased and exposed 4
} else {
etat[i,j] <- "01"; # non diseased and exposed 1
}
} else {
if (etat[i,j-1] == "03") {
if (alea0 <= data_incid_3_values[which(data_incid_3_values[,1]%in%(j-1+65) & data_incid_3_values[,3]%in%(gender)),2]) {
etat[i,j] <- "06"; # non diseased and exposed 6
} else {
etat[i,j] <- "03"; # non diseased and exposed 3
}
} else {
if (etat[i,j-1] == "05") {
if (alea0 <= data_incid_5_values[which(data_incid_5_values[,1]%in%(j-1+65) & data_incid_5_values[,3]%in%(gender)),2]) {
etat[i,j] <- "07"; # non diseased and exposed 7
} else {
etat[i,j] <- "05"; # non diseased and exposed 5
}
} else {
if (etat[i,j-1] == "02") {
etat[i,j] <- "02"; # non diseased and exposed 2
} else {
if (etat[i,j-1] == "04") {
etat[i,j] <- "04"; # non diseased and exposed 4
} else {
if (etat[i,j-1] == "06") {
etat[i,j] <- "06"; # non diseased and exposed 6
} else {
if (etat[i,j-1] == "07") {
etat[i,j] <- "07"; # non diseased and exposed 7
} else {
if (etat[i,j-1] == "10") {
if (alea0 <= data_incid_0_values[which(data_incid_0_values[,1]%in%(j-1+65) & data_incid_0_values[,3]%in%(gender)),2]) {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
etat[i,j] <- "10"; # diseased and non exposed
}
} else {
if (etat[i,j-1] == "11") {
if (alea0 <= data_incid_1_values[which(data_incid_1_values[,1]%in%(j-1+65) & data_incid_1_values[,3]%in%(gender)),2]) {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
etat[i,j] <- "11"; # diseased and exposed 1
}
} else {
if (etat[i,j-1] == "13") {
if (alea0 <= data_incid_3_values[which(data_incid_3_values[,1]%in%(j-1+65) & data_incid_3_values[,3]%in%(gender)),2]) {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
etat[i,j] <- "13"; # diseased and exposed 3
}
} else {
if (etat[i,j-1] == "15") {
if (alea0 <= data_incid_5_values[which(data_incid_5_values[,1]%in%(j-1+65) & data_incid_5_values[,3]%in%(gender)),2]) {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- "15"; # diseased and exposed 5
}
} else {
if (etat[i,j-1] == "12") {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
if (etat[i,j-1] == "14") {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
if (etat[i,j-1] == "16") {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
if (etat[i,j-1] == "17") {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- etat[i,j-1]
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
if (sum(etat[,j]%in%("00"))!=0) {
data_prev_0_values_ND[which(data_prev_0_values_ND[,1]%in%(j+64) & data_prev_0_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("00"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("10"))!=0) {
data_prev_0_values_D[which(data_prev_0_values_D[,1]%in%(j+64) & data_prev_0_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("10"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("01"))!=0) {
data_prev_1_values_ND[which(data_prev_1_values_ND[,1]%in%(j+64) & data_prev_1_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("01"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("11"))!=0) {
data_prev_1_values_D[which(data_prev_1_values_D[,1]%in%(j+64) & data_prev_1_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("11"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("02"))!=0) {
data_prev_2_values_ND[which(data_prev_2_values_ND[,1]%in%(j+64) & data_prev_2_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("02"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("12"))!=0) {
data_prev_2_values_D[which(data_prev_2_values_D[,1]%in%(j+64) & data_prev_2_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("12"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("03"))!=0) {
data_prev_3_values_ND[which(data_prev_3_values_ND[,1]%in%(j+64) & data_prev_3_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("03"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("13"))!=0) {
data_prev_3_values_D[which(data_prev_3_values_D[,1]%in%(j+64) & data_prev_3_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("13"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("04"))!=0) {
data_prev_4_values_ND[which(data_prev_4_values_ND[,1]%in%(j+64) & data_prev_4_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("04"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("14"))!=0) {
data_prev_4_values_D[which(data_prev_4_values_D[,1]%in%(j+64) & data_prev_4_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("14"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("05"))!=0) {
data_prev_5_values_ND[which(data_prev_5_values_ND[,1]%in%(j+64) & data_prev_5_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("05"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("15"))!=0) {
data_prev_5_values_D[which(data_prev_5_values_D[,1]%in%(j+64) & data_prev_5_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("15"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("06"))!=0) {
data_prev_6_values_ND[which(data_prev_6_values_ND[,1]%in%(j+64) & data_prev_6_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("06"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("16"))!=0) {
data_prev_6_values_D[which(data_prev_6_values_D[,1]%in%(j+64) & data_prev_6_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("16"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
if (sum(etat[,j]%in%("07"))!=0) {
data_prev_7_values_ND[which(data_prev_7_values_ND[,1]%in%(j+64) & data_prev_7_values_ND[,3]%in%(gender)),2] <- sum(etat[,j]%in%("07"))/sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")))
}
if (sum(etat[,j]%in%("17"))!=0) {
data_prev_7_values_D[which(data_prev_7_values_D[,1]%in%(j+64) & data_prev_7_values_D[,3]%in%(gender)),2] <- sum(etat[,j]%in%("17"))/sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")))
}
### Incidence of disease on non exposed peoples
for (a in 2:ncol(a010_values)){
a010_values[,a] <- as.numeric(data_a01_values[which(data_a01_values[,1] != 65 & data_a01_values[,2]%in%(gender)),a+1]) / (data_prev_0_values_ND[which(data_prev_0_values_ND[,1] != 65 & data_prev_0_values_ND[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_prev_1_values_ND[which(data_prev_1_values_ND[,1] != 65 & data_prev_1_values_ND[,3]%in%(gender)),2] + data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_prev_2_values_ND[which(data_prev_2_values_ND[,1] != 65 & data_prev_2_values_ND[,3]%in%(gender)),2] + data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_3_values_ND[which(data_prev_3_values_ND[,1] != 65 & data_prev_3_values_ND[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_prev_4_values_ND[which(data_prev_4_values_ND[,1] != 65 & data_prev_4_values_ND[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_5_values_ND[which(data_prev_5_values_ND[,1] != 65 & data_prev_5_values_ND[,3]%in%(gender)),2] + data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_6_values_ND[which(data_prev_6_values_ND[,1] != 65 & data_prev_6_values_ND[,3]%in%(gender)),2] + data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2]*data_prev_7_values_ND[which(data_prev_7_values_ND[,1] != 65 & data_prev_7_values_ND[,3]%in%(gender)),2]);
}
### Incidence of disease on exposed peoples
a011_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2];
a012_values[,-1] <- a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2];
a013_values[,-1] <- a010_values[,-1]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a014_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2];
a015_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a016_values[,-1] <- a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
a017_values[,-1] <- a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
### Global incidence of disease
a01_global_values[,-1] <- data_prev_0_values_ND[which(data_prev_0_values_ND[,1] != 65 & data_prev_0_values_ND[,3]%in%(gender)),2]*a010_values[,-1] + data_prev_1_values_ND[which(data_prev_1_values_ND[,1] != 65 & data_prev_1_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2] + data_prev_2_values_ND[which(data_prev_2_values_ND[,1] != 65 & data_prev_2_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2] + data_prev_3_values_ND[which(data_prev_3_values_ND[,1] != 65 & data_prev_3_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_4_values_ND[which(data_prev_4_values_ND[,1] != 65 & data_prev_4_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2] + data_prev_5_values_ND[which(data_prev_5_values_ND[,1] != 65 & data_prev_5_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_6_values_ND[which(data_prev_6_values_ND[,1] != 65 & data_prev_6_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2] + data_prev_7_values_ND[which(data_prev_7_values_ND[,1] != 65 & data_prev_7_values_ND[,3]%in%(gender)),2]*a010_values[,-1]*data_theta01_1_values[which(data_theta01_1_values[,1] != 65 & data_theta01_1_values[,3]%in%(gender)),2]*data_theta01_2_values[which(data_theta01_2_values[,1] != 65 & data_theta01_2_values[,3]%in%(gender)),2]*data_theta01_3_values[which(data_theta01_3_values[,1] != 65 & data_theta01_3_values[,3]%in%(gender)),2];
### Mortality of healthy subjects on non exposed peoples
for (a in 2:ncol(a020_values)){
a020_values[,a] <- as.numeric(data_a02_values[which(data_a02_values[,1] != 65 & data_a02_values[,2]%in%(gender)),a+1]) / (data_prev_0_values_ND[which(data_prev_0_values_ND[,1] != 65 & data_prev_0_values_ND[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_prev_1_values_ND[which(data_prev_1_values_ND[,1] != 65 & data_prev_1_values_ND[,3]%in%(gender)),2] + data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_prev_2_values_ND[which(data_prev_2_values_ND[,1] != 65 & data_prev_2_values_ND[,3]%in%(gender)),2] + data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_3_values_ND[which(data_prev_3_values_ND[,1] != 65 & data_prev_3_values_ND[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_prev_4_values_ND[which(data_prev_4_values_ND[,1] != 65 & data_prev_4_values_ND[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_5_values_ND[which(data_prev_5_values_ND[,1] != 65 & data_prev_5_values_ND[,3]%in%(gender)),2] + data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_6_values_ND[which(data_prev_6_values_ND[,1] != 65 & data_prev_6_values_ND[,3]%in%(gender)),2] + data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2]*data_prev_7_values_ND[which(data_prev_7_values_ND[,1] != 65 & data_prev_7_values_ND[,3]%in%(gender)),2]);
}
### Mortality of healthy subjects on exposed peoples
a021_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2];
a022_values[,-1] <- a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2];
a023_values[,-1] <- a020_values[,-1]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a024_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2];
a025_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a026_values[,-1] <- a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
a027_values[,-1] <- a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
### Global mortality of healthy subjects
a02_global_values[,-1] <- data_prev_0_values_ND[which(data_prev_0_values_ND[,1] != 65 & data_prev_0_values_ND[,3]%in%(gender)),2]*a020_values[,-1] + data_prev_1_values_ND[which(data_prev_1_values_ND[,1] != 65 & data_prev_1_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2] + data_prev_2_values_ND[which(data_prev_2_values_ND[,1] != 65 & data_prev_2_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2] + data_prev_3_values_ND[which(data_prev_3_values_ND[,1] != 65 & data_prev_3_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_4_values_ND[which(data_prev_4_values_ND[,1] != 65 & data_prev_4_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2] + data_prev_5_values_ND[which(data_prev_5_values_ND[,1] != 65 & data_prev_5_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_6_values_ND[which(data_prev_6_values_ND[,1] != 65 & data_prev_6_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2] + data_prev_7_values_ND[which(data_prev_7_values_ND[,1] != 65 & data_prev_7_values_ND[,3]%in%(gender)),2]*a020_values[,-1]*data_theta02_1_values[which(data_theta02_1_values[,1] != 65 & data_theta02_1_values[,3]%in%(gender)),2]*data_theta02_2_values[which(data_theta02_2_values[,1] != 65 & data_theta02_2_values[,3]%in%(gender)),2]*data_theta02_3_values[which(data_theta02_3_values[,1] != 65 & data_theta02_3_values[,3]%in%(gender)),2];
### Relative risks associated with the disease for mortality
RR_values[,2] <- data_rr_DvsND_values[which(data_rr_DvsND_values[,1] != 65 & data_rr_DvsND_values[,3]%in%(gender)),2];
### Mortality of diseased subjects on non exposed peoples
for (a in 2:ncol(a120_values)){
a120_values[,a] <- as.numeric(RR_values[,2])*as.numeric(data_a02_values[which(data_a02_values[,1] != 65 & data_a02_values[,2]%in%(gender)),a+1]) / (data_prev_0_values_D[which(data_prev_0_values_D[,1] != 65 & data_prev_0_values_D[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_prev_1_values_D[which(data_prev_1_values_D[,1] != 65 & data_prev_1_values_D[,3]%in%(gender)),2] + data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_prev_2_values_D[which(data_prev_2_values_D[,1] != 65 & data_prev_2_values_D[,3]%in%(gender)),2] + data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_3_values_D[which(data_prev_3_values_D[,1] != 65 & data_prev_3_values_D[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_prev_4_values_D[which(data_prev_4_values_D[,1] != 65 & data_prev_4_values_D[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_5_values_D[which(data_prev_5_values_D[,1] != 65 & data_prev_5_values_D[,3]%in%(gender)),2] + data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_6_values_D[which(data_prev_6_values_D[,1] != 65 & data_prev_6_values_D[,3]%in%(gender)),2] + data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2]*data_prev_7_values_D[which(data_prev_7_values_D[,1] != 65 & data_prev_7_values_D[,3]%in%(gender)),2]);
}
### Mortality of diseased subjects on exposed peoples
a121_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2];
a122_values[,-1] <- a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2];
a123_values[,-1] <- a120_values[,-1]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a124_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2];
a125_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a126_values[,-1] <- a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
a127_values[,-1] <- a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
### Global mortality of diseased subjects
a12_global_values[,-1] <- data_prev_0_values_D[which(data_prev_0_values_D[,1] != 65 & data_prev_0_values_D[,3]%in%(gender)),2]*a120_values[,-1] + data_prev_1_values_D[which(data_prev_1_values_D[,1] != 65 & data_prev_1_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2] + data_prev_2_values_D[which(data_prev_2_values_D[,1] != 65 & data_prev_2_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2] + data_prev_3_values_D[which(data_prev_3_values_D[,1] != 65 & data_prev_3_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_4_values_D[which(data_prev_4_values_D[,1] != 65 & data_prev_4_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2] + data_prev_5_values_D[which(data_prev_5_values_D[,1] != 65 & data_prev_5_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_6_values_D[which(data_prev_6_values_D[,1] != 65 & data_prev_6_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2] + data_prev_7_values_D[which(data_prev_7_values_D[,1] != 65 & data_prev_7_values_D[,3]%in%(gender)),2]*a120_values[,-1]*data_theta12_1_values[which(data_theta12_1_values[,1] != 65 & data_theta12_1_values[,3]%in%(gender)),2]*data_theta12_2_values[which(data_theta12_2_values[,1] != 65 & data_theta12_2_values[,3]%in%(gender)),2]*data_theta12_3_values[which(data_theta12_3_values[,1] != 65 & data_theta12_3_values[,3]%in%(gender)),2];
for (i in 1:nrow(etat)) { # for each age
alea <- runif(1, 0, 1);
alea0 <- runif(1, 0, 1);
if (etat[i,j] == "00") {
a01 <- a010_values;
a02 <- a020_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "20"; # dead (with non exposed state)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "10"; # diseased and non exposed
} else {
etat[i,j] <- "00"; # non diseased and non exposed
}
}
} else {
if (etat[i,j] == "01") {
a01 <- a011_values;
a02 <- a021_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "21"; # dead (with exposed state 1)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "11"; # diseased and exposed 1
} else {
etat[i,j] <- "01"; # non diseased and exposed 1
}
}
} else {
if (etat[i,j] == "02") {
a01 <- a012_values;
a02 <- a022_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "22"; # dead (with exposed state 2)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
etat[i,j] <- "02"; # non diseased and exposed 2
}
}
} else {
if (etat[i,j] == "03") {
a01 <- a013_values;
a02 <- a023_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "23"; # dead (with exposed state 3)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "13"; # diseased and exposed 3
} else {
etat[i,j] <- "03"; # non diseased and exposed 3
}
}
} else {
if (etat[i,j] == "04") {
a01 <- a014_values;
a02 <- a024_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "24"; # dead (with exposed state 4)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
etat[i,j] <- "04"; # non diseased and exposed 4
}
};
} else {
if (etat[i,j] == "05") {
a01 <- a015_values;
a02 <- a025_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "25"; # dead (with exposed state 5)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "15"; # diseased and exposed 5
} else {
etat[i,j] <- "05"; # non diseased and exposed 5
}
}
} else {
if (etat[i,j] == "06") {
a01 <- a016_values;
a02 <- a026_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "26"; # dead (with exposed state 6)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
etat[i,j] <- "06"; # non diseased and exposed 6
}
};
} else {
if (etat[i,j] == "07") {
a01 <- a017_values;
a02 <- a027_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "27"; # dead (with exposed state 7)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- "07"; # non diseased and exposed 7
}
};
} else {
if (etat[i,j] == "10") {
a12 <- a120_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "20";
} else {
etat[i,j] <- "10";
}
} else {
if (etat[i,j] == "11") {
a12 <- a121_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "21";
} else {
etat[i,j] <- "11";
}
} else {
if (etat[i,j] == "12") {
a12 <- a122_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "22";
} else {
etat[i,j] <- "12";
}
} else {
if (etat[i,j] == "13") {
a12 <- a123_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "23";
} else {
etat[i,j] <- "13";
}
} else {
if (etat[i,j] == "14") {
a12 <- a124_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "24";
} else {
etat[i,j] <- "14";
}
} else {
if (etat[i,j] == "15") {
a12 <- a125_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "25";
} else {
etat[i,j] <- "15";
}
} else {
if (etat[i,j] == "16") {
a12 <- a126_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "26";
} else {
etat[i,j] <- "16";
}
} else {
if (etat[i,j] == "17") {
a12 <- a127_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "27";
} else {
etat[i,j] <- "17";
}
} else {
etat[i,j] <- etat[i,j]
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
# RUN 2
for (i in 1:nrow(etat)) {
if (intervention==1) {
if (annee < year_intervention) {
intervention_prev_0_values <- data_prev_0_values
intervention_prev_1_values <- data_prev_1_values
intervention_prev_2_values <- data_prev_2_values
intervention_prev_3_values <- data_prev_3_values
intervention_prev_4_values <- data_prev_4_values
intervention_prev_5_values <- data_prev_5_values
intervention_prev_6_values <- data_prev_6_values
intervention_prev_7_values <- data_prev_7_values
} else {
intervention_prev_0_values <- data_prev_0_values
intervention_prev_0_values[,2] <- 1
intervention_prev_1_values <- data_prev_1_values
intervention_prev_1_values[,2] <- 0
intervention_prev_2_values <- data_prev_2_values
intervention_prev_2_values[,2] <- 0
intervention_prev_3_values <- data_prev_3_values
intervention_prev_3_values[,2] <- 0
intervention_prev_4_values <- data_prev_4_values
intervention_prev_4_values[,2] <- 0
intervention_prev_5_values <- data_prev_5_values
intervention_prev_5_values[,2] <- 0
intervention_prev_6_values <- data_prev_6_values
intervention_prev_6_values[,2] <- 0
intervention_prev_7_values <- data_prev_7_values
intervention_prev_7_values[,2] <- 0
}
}
alea0 <- runif(1, 0, 1);
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]) {
etat[i,1] <- "00" # non diseased and non exposed
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]) {
etat[i,1] <- "01" # non diseased and exposed 1
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]+intervention_prev_2_values[which(intervention_prev_2_values[,1]%in%(65) & intervention_prev_2_values[,3]%in%(gender)),2]) {
etat[i,1] <- "02" # non diseased and exposed 2
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]+intervention_prev_2_values[which(intervention_prev_2_values[,1]%in%(65) & intervention_prev_2_values[,3]%in%(gender)),2]+intervention_prev_3_values[which(intervention_prev_3_values[,1]%in%(65) & intervention_prev_3_values[,3]%in%(gender)),2]) {
etat[i,1] <- "03" # non diseased and exposed 3
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]+intervention_prev_2_values[which(intervention_prev_2_values[,1]%in%(65) & intervention_prev_2_values[,3]%in%(gender)),2]+intervention_prev_3_values[which(intervention_prev_3_values[,1]%in%(65) & intervention_prev_3_values[,3]%in%(gender)),2]+intervention_prev_4_values[which(intervention_prev_4_values[,1]%in%(65) & intervention_prev_4_values[,3]%in%(gender)),2]) {
etat[i,1] <- "04" # non diseased and exposed 4
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]+intervention_prev_2_values[which(intervention_prev_2_values[,1]%in%(65) & intervention_prev_2_values[,3]%in%(gender)),2]+intervention_prev_3_values[which(intervention_prev_3_values[,1]%in%(65) & intervention_prev_3_values[,3]%in%(gender)),2]+intervention_prev_4_values[which(intervention_prev_4_values[,1]%in%(65) & intervention_prev_4_values[,3]%in%(gender)),2]+intervention_prev_5_values[which(intervention_prev_5_values[,1]%in%(65) & intervention_prev_5_values[,3]%in%(gender)),2]) {
etat[i,1] <- "05" # non diseased and exposed 5
} else {
if (alea0 <= intervention_prev_0_values[which(intervention_prev_0_values[,1]%in%(65) & intervention_prev_0_values[,3]%in%(gender)),2]+intervention_prev_1_values[which(intervention_prev_1_values[,1]%in%(65) & intervention_prev_1_values[,3]%in%(gender)),2]+intervention_prev_2_values[which(intervention_prev_2_values[,1]%in%(65) & intervention_prev_2_values[,3]%in%(gender)),2]+intervention_prev_3_values[which(intervention_prev_3_values[,1]%in%(65) & intervention_prev_3_values[,3]%in%(gender)),2]+intervention_prev_4_values[which(intervention_prev_4_values[,1]%in%(65) & intervention_prev_4_values[,3]%in%(gender)),2]+intervention_prev_5_values[which(intervention_prev_5_values[,1]%in%(65) & intervention_prev_5_values[,3]%in%(gender)),2]+intervention_prev_6_values[which(intervention_prev_6_values[,1]%in%(65) & intervention_prev_6_values[,3]%in%(gender)),2]) {
etat[i,1] <- "06" # non diseased and exposed 6
} else {
etat[i,1] <- "07" # non diseased and exposed 7
}
}
}
}
}
}
}
};
for (j in 2:ncol(etat)) { # for each people
if (intervention==1) {
if (annee+j-1 < year_intervention) {
intervention_incid_0_values <- data_incid_0_values
intervention_incid_1_values <- data_incid_1_values
intervention_incid_3_values <- data_incid_3_values
intervention_incid_5_values <- data_incid_5_values
} else {
intervention_incid_0_values <- data_incid_0_values
intervention_incid_0_values[,2] <- 0
intervention_incid_1_values <- data_incid_1_values
intervention_incid_1_values[,2] <- 0
intervention_incid_3_values <- data_incid_3_values
intervention_incid_3_values[,2] <- 0
intervention_incid_5_values <- data_incid_5_values
intervention_incid_5_values[,2] <- 0
}
}
for (i in 1:nrow(etat)) { # for each age
alea <- runif(1, 0, 1);
alea0 <- runif(1, 0, 1);
if (etat[i,j-1] == "00") {
if (alea0 <= intervention_incid_0_values[which(intervention_incid_0_values[,1]%in%(j-1+65) & intervention_incid_0_values[,3]%in%(gender)),2]) {
etat[i,j] <- "02"; # non diseased and exposed 2
} else {
etat[i,j] <- "00"; # non diseased and non exposed
}
} else {
if (etat[i,j-1] == "01") {
if (alea0 <= intervention_incid_1_values[which(intervention_incid_1_values[,1]%in%(j-1+65) & intervention_incid_1_values[,3]%in%(gender)),2]) {
etat[i,j] <- "04"; # non diseased and exposed 4
} else {
etat[i,j] <- "01"; # non diseased and exposed 1
}
} else {
if (etat[i,j-1] == "03") {
if (alea0 <= intervention_incid_3_values[which(intervention_incid_3_values[,1]%in%(j-1+65) & intervention_incid_3_values[,3]%in%(gender)),2]) {
etat[i,j] <- "06"; # non diseased and exposed 6
} else {
etat[i,j] <- "03"; # non diseased and exposed 3
}
} else {
if (etat[i,j-1] == "05") {
if (alea0 <= intervention_incid_5_values[which(intervention_incid_5_values[,1]%in%(j-1+65) & intervention_incid_5_values[,3]%in%(gender)),2]) {
etat[i,j] <- "07"; # non diseased and exposed 7
} else {
etat[i,j] <- "05"; # non diseased and exposed 5
}
} else {
if (etat[i,j-1] == "02") {
etat[i,j] <- "02"; # non diseased and exposed 2
} else {
if (etat[i,j-1] == "04") {
etat[i,j] <- "04"; # non diseased and exposed 4
} else {
if (etat[i,j-1] == "06") {
etat[i,j] <- "06"; # non diseased and exposed 6
} else {
if (etat[i,j-1] == "07") {
etat[i,j] <- "07"; # non diseased and exposed 7
} else {
if (etat[i,j-1] == "10") {
if (alea0 <= intervention_incid_0_values[which(intervention_incid_0_values[,1]%in%(j-1+65) & intervention_incid_0_values[,3]%in%(gender)),2]) {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
etat[i,j] <- "10"; # diseased and non exposed
}
} else {
if (etat[i,j-1] == "11") {
if (alea0 <= intervention_incid_1_values[which(intervention_incid_1_values[,1]%in%(j-1+65) & intervention_incid_1_values[,3]%in%(gender)),2]) {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
etat[i,j] <- "11"; # diseased and exposed 1
}
} else {
if (etat[i,j-1] == "13") {
if (alea0 <= intervention_incid_3_values[which(intervention_incid_3_values[,1]%in%(j-1+65) & intervention_incid_3_values[,3]%in%(gender)),2]) {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
etat[i,j] <- "13"; # diseased and exposed 3
}
} else {
if (etat[i,j-1] == "15") {
if (alea0 <= intervention_incid_5_values[which(intervention_incid_5_values[,1]%in%(j-1+65) & intervention_incid_5_values[,3]%in%(gender)),2]) {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- "15"; # diseased and exposed 5
}
} else {
if (etat[i,j-1] == "12") {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
if (etat[i,j-1] == "14") {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
if (etat[i,j-1] == "16") {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
if (etat[i,j-1] == "17") {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- etat[i,j-1]
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
for (i in 1:nrow(etat)) { # for each age
alea <- runif(1, 0, 1);
alea0 <- runif(1, 0, 1);
if (etat[i,j] == "00") {
a01 <- a010_values;
a02 <- a020_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "20"; # dead (with non exposed state)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "10"; # diseased and non exposed
} else {
etat[i,j] <- "00"; # non diseased and non exposed
}
}
} else {
if (etat[i,j] == "01") {
a01 <- a011_values;
a02 <- a021_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "21"; # dead (with exposed state 1)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "11"; # diseased and exposed 1
} else {
etat[i,j] <- "01"; # non diseased and exposed 1
}
}
} else {
if (etat[i,j] == "02") {
a01 <- a012_values;
a02 <- a022_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "22"; # dead (with exposed state 2)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "12"; # diseased and exposed 2
} else {
etat[i,j] <- "02"; # non diseased and exposed 2
}
}
} else {
if (etat[i,j] == "03") {
a01 <- a013_values;
a02 <- a023_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "23"; # dead (with exposed state 3)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "13"; # diseased and exposed 3
} else {
etat[i,j] <- "03"; # non diseased and exposed 3
}
}
} else {
if (etat[i,j] == "04") {
a01 <- a014_values;
a02 <- a024_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "24"; # dead (with exposed state 4)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "14"; # diseased and exposed 4
} else {
etat[i,j] <- "04"; # non diseased and exposed 4
}
};
} else {
if (etat[i,j] == "05") {
a01 <- a015_values;
a02 <- a025_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "25"; # dead (with exposed state 5)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "15"; # diseased and exposed 5
} else {
etat[i,j] <- "05"; # non diseased and exposed 5
}
}
} else {
if (etat[i,j] == "06") {
a01 <- a016_values;
a02 <- a026_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "26"; # dead (with exposed state 6)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "16"; # diseased and exposed 6
} else {
etat[i,j] <- "06"; # non diseased and exposed 6
}
};
} else {
if (etat[i,j] == "07") {
a01 <- a017_values;
a02 <- a027_values;
if (alea <= a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "27"; # dead (with exposed state 7)
} else {
if (alea <= a01[j-1,j+(an0-1950)+1] + a02[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "17"; # diseased and exposed 7
} else {
etat[i,j] <- "07"; # non diseased and exposed 7
}
};
} else {
if (etat[i,j] == "10") {
a12 <- a120_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "20";
} else {
etat[i,j] <- "10";
}
} else {
if (etat[i,j] == "11") {
a12 <- a121_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "21";
} else {
etat[i,j] <- "11";
}
} else {
if (etat[i,j] == "12") {
a12 <- a122_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "22";
} else {
etat[i,j] <- "12";
}
} else {
if (etat[i,j] == "13") {
a12 <- a123_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "23";
} else {
etat[i,j] <- "13";
}
} else {
if (etat[i,j] == "14") {
a12 <- a124_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "24";
} else {
etat[i,j] <- "14";
}
} else {
if (etat[i,j] == "15") {
a12 <- a125_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "25";
} else {
etat[i,j] <- "15";
}
} else {
if (etat[i,j] == "16") {
a12 <- a126_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "26";
} else {
etat[i,j] <- "16";
}
} else {
if (etat[i,j] == "17") {
a12 <- a127_values;
if (alea <= a12[j-1,j+(an0-1950)+1]) {
etat[i,j] <- "27";
} else {
etat[i,j] <- "17";
}
} else {
etat[i,j] <- etat[i,j]
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
### Computation of health indicators :
### Overall life-expectancy
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")))
};
esp_vie_gen[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Overall life-expectancy on exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("01") | etat[,age-64]%in%("11")),age-64]%in%("01") | etat[which(etat[,age-64]%in%("01") | etat[,age-64]%in%("11")),age-64]%in%("11"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("01") | etat[,age-64]%in%("11")),j]%in%("01") | etat[which(etat[,age-64]%in%("01") | etat[,age-64]%in%("11")),j]%in%("11"))
};
esp_vie_gen_conso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Overall life-expectancy on non exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),age-64]%in%("00") | etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),age-64]%in%("10"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),j]%in%("00") | etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),j]%in%("10") | etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),j]%in%("01") | etat[which(etat[,age-64]%in%("00") | etat[,age-64]%in%("10")),j]%in%("11"))
};
esp_vie_gen_nonconso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy without disease
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07"))),age-64]%in%(c("00","01","02","03","04","05","06","07")))
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07"))),j]%in%(c("00","01","02","03","04","05","06","07")))
};
esp_vie_sans_mal[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy without disease on exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("01")),age-64]%in%("01"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("01")),j]%in%("01"))
};
esp_vie_sans_mal_conso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy without disease on non exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("00")),age-64]%in%("00"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("00")),j]%in%("00") | etat[which(etat[,age-64]%in%("00")),j]%in%("01"))
};
esp_vie_sans_mal_nonconso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for diseased subject
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("10") | etat[,age-64]%in%("11")),age-64]%in%("10") | etat[which(etat[,age-64]%in%("10") | etat[,age-64]%in%("11")),age-64]%in%("11"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("10") | etat[,age-64]%in%("11")),j]%in%("10") | etat[which(etat[,age-64]%in%("10") | etat[,age-64]%in%("11")),j]%in%("11"))
};
esp_vie_mal[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for diseased subject on exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("11")),age-64]%in%("11"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("11")),j]%in%("11"))
};
esp_vie_mal_conso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for diseased subject on non exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("10")),age-64]%in%("10"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("10")),j]%in%("10") | etat[which(etat[,age-64]%in%("10")),j]%in%("11"))
};
esp_vie_mal_nonconso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for non diseased subject
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07"))),age-64]%in%(c("00","01","02","03","04","05","06","07")));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07"))),j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")))
};
esp_vie_non_mal[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for non diseased subject on exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("01")),age-64]%in%("01"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("01")),j]%in%("01") | etat[which(etat[,age-64]%in%("01")),j]%in%("11"))
};
esp_vie_non_mal_conso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Life-expectancy for non diseased subject on non exposed peoples
if (age < 101) {
n0 <- vector(length = ncol(etat));
s0 <- sum(etat[which(etat[,age-64]%in%("00")),age-64]%in%("00"));
if (s0 != 0) {
for (j in (age-63):ncol(etat)) {
n0[j] <- sum(etat[which(etat[,age-64]%in%("00")),j]%in%("00") | etat[which(etat[,age-64]%in%("00")),j]%in%("01") | etat[which(etat[,age-64]%in%("00")),j]%in%("10") | etat[which(etat[,age-64]%in%("00")),j]%in%("11"))
};
esp_vie_non_mal_nonconso[age-64,2] <- 1 + sum(n0) / s0;
} else {
n0 <- NA;
}
};
### Prevalence of disease
if (age > 65 & age < 100) {
d0 <- sum(etat[,age-64]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17"))) + 0.5 * sum((etat[,age-64]%in%(c("20","21","22","23","24","25","26","27"))) & (etat[,age-65]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17"))));
s0 <- sum(etat[,1]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum((etat[,age-64]%in%(c("10","11","12","13","14","15","16","17"))) & (etat[,age-65]%in%(c("10","11","12","13","14","15","16","17")))) + 0.5 * sum((etat[,age-64]%in%(c("10","11","12","13","14","15","16","17"))) & (etat[,age-65]%in%(c("00","01","02","03","04","05","06","07")))) + 0.5 * sum((etat[,age-64]%in%(c("20","21","22","23","24","25","26","27"))) & (etat[,age-65]%in%(c("10","11","12","13","14","15","16","17"))));
if (s0 != 0) {
p01 <- s1/s0;
taux_prevalence[age-64,2] <- s1/d0;
nb <- p01*data_pop[which(data_pop[,1]%in%(an0) & data_pop[,3]%in%(gender)),2];
prevalence[age-64,2] <- nb;
};
};
### Survival
if (age == 65) {
survie[age-64,2] <- data_pop[which(data_pop[,1]%in%(an0) & data_pop[,3]%in%(gender)),2]
};
if (age > 65 & age < 100) {
d0 <- sum(etat[,age-65]%in%("00") | etat[,age-65]%in%("01") | etat[,age-65]%in%("10") | etat[,age-65]%in%("11"));
s0 <- sum(etat[,1]%in%("00") | etat[,1]%in%("01") | etat[,1]%in%("10") | etat[,1]%in%("11"));
s1 <- sum(etat[,age-64]%in%("00") | etat[,age-64]%in%("01") | etat[,age-64]%in%("10") | etat[,age-64]%in%("11"));
if (s0 != 0) {
p01 <- s1/s0;
taux_survivants[age-64,2] <- s1/d0;
nb <- p01*data_pop[which(data_pop[,1]%in%(an0) & data_pop[,3]%in%(gender)),2];
survie[age-64,2] <- nb;
};
};
### Mean number of years spent with disease
if (age < 101) {
nb_moy_dem[age-64,2] <- esp_vie_non_mal[age-64,2] - esp_vie_sans_mal[age-64,2]
} else {
n0 <- NA
};
### Mean number of years spent with disease on exposed peoples
if (age < 101) {
nb_moy_dem_conso[age-64,2] <- esp_vie_non_mal_conso[age-64,2] - esp_vie_sans_mal_conso[age-64,2];
} else {
n0 <- NA;
};
### Mean number of years spent with disease on non exposed peoples
if (age < 101) {
nb_moy_dem_nonconso[age-64,2] <- esp_vie_non_mal_nonconso[age-64,2] - esp_vie_sans_mal_nonconso[age-64,2];
} else {
n0 <- NA;
};
### Life-long probability of disease
if (age == 65) {
for (i in (age-63):nrow(prb_dem)) {
prb_dem[i,2] <- sum(etat[which(etat[,i-1]%in%(c("00","01","02","03","04","05","06","07"))),i]%in%(c("10","11","12","13","14","15","16","17")))
}
};
### Average age at disease onset
if (age == 65) {
for (i in (age-63):nrow(age_dem)) {
age_dem[i,2] <- sum(etat[which(etat[,i-1]%in%(c("00","01","02","03","04","05","06","07"))),i]%in%(c("10","11","12","13","14","15","16","17")))
}
};
### Mean number of years of exposition
if (age == 65) {
for (i in (age-63):nrow(age_dem)) {
moy_conso[i,2] <- sum(etat[which(etat[,1]%in%("00") | etat[,1]%in%("10")),i]%in%("01") | etat[which(etat[,1]%in%("00") | etat[,1]%in%("10")),i]%in%("11"))
}
};
### Number of exposed peoples at least one time
if (age <= 105) {
s0 <- sum(etat[,age-64]%in%("00") | etat[,age-64]%in%("10") | etat[,age-64]%in%("01") | etat[,age-64]%in%("11"));
s1 <- sum(etat[,age-64]%in%("01") | etat[,age-64]%in%("11"));
if (s0 != 0) {
prevalence_conso[age-64,2] <- s1/s0;
} else {
prevalence_conso[age-64,2] <- NA;
};
};
### Mortality rate
if (age > 65 & age < 100) {
s0 <- sum(etat[,age-64]%in%("00") | etat[,age-64]%in%("10") | etat[,age-64]%in%("01") | etat[,age-64]%in%("11"));
s1 <- sum(etat[,age-64]%in%("20") | etat[,age-64]%in%("21")) - sum(etat[,age-65]%in%("20") | etat[,age-65]%in%("21"));
if (s0 != 0) {
quotient_mortalite[age-64,2] <- s1/s0;
} else {
quotient_mortalite[age-64,2] <- NA;
};
};
##############################
########## ESSAIS ############
##############################
### Dementia incidence
if (age >= 65) {
for (j in 1:nrow(incid_demence)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")));
s1 <- sum((etat[,j+1]%in%(c("10","11","12","13","14","15","16","17"))) & (etat[,j]%in%(c("00","01","02","03","04","05","06","07"))));
if (s0 != 0) {
incid_demence[j,age-63] <- s1/s0;
};
}
};
### Mortality incidence
if (age >= 65) {
for (j in 1:nrow(incid_mort_NoD)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07")));
s1 <- sum((etat[,j+1]%in%(c("20","21","22","23","24","25","26","27"))) & (etat[,j]%in%(c("00","01","02","03","04","05","06","07"))));
if (s0 != 0) {
incid_mort_NoD[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(incid_mort_Dem)) {
s0 <- sum(etat[,j]%in%(c("10","11","12","13","14","15","16","17")));
s1 <- sum((etat[,j+1]%in%(c("20","21","22","23","24","25","26","27"))) & (etat[,j]%in%(c("10","11","12","13","14","15","16","17"))));
if (s0 != 0) {
incid_mort_Dem[j,age-63] <- s1/s0;
};
}
};
### Non exposed prevalence
if (age >= 65) {
for (j in 1:nrow(prevalence_noe)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("00","10")));
if (s0 != 0) {
prevalence_noe[j,age-63] <- s1/s0;
};
}
};
### Hypertension prevalence
if (age >= 65) {
for (j in 1:nrow(prevalence_hta)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("01","04","05","07","11","14","15","17")));
if (s0 != 0) {
prevalence_hta[j,age-63] <- s1/s0;
};
}
};
### Physical inactivity prevalence
if (age >= 65) {
for (j in 1:nrow(prevalence_ina)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("03","05","06","07","13","15","16","17")));
if (s0 != 0) {
prevalence_ina[j,age-63] <- s1/s0;
};
}
};
### Diabete prevalence
if (age >= 65) {
for (j in 1:nrow(prevalence_dia)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("02","04","06","07","12","14","16","17")));
if (s0 != 0) {
prevalence_dia[j,age-63] <- s1/s0;
};
}
};
### Expositions prevalence
if (age >= 65) {
for (j in 1:nrow(prevalence_exp0)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("00","10")));
if (s0 != 0) {
prevalence_exp0[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp1)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("01","11")));
if (s0 != 0) {
prevalence_exp1[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp2)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("02","12")));
if (s0 != 0) {
prevalence_exp2[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp3)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("03","13")));
if (s0 != 0) {
prevalence_exp3[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp4)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("04","14")));
if (s0 != 0) {
prevalence_exp4[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp5)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("05","15")));
if (s0 != 0) {
prevalence_exp5[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp6)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("06","16")));
if (s0 != 0) {
prevalence_exp6[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prevalence_exp7)) {
s0 <- sum(etat[,j]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j]%in%(c("07","17")));
if (s0 != 0) {
prevalence_exp7[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prop_dem_global)) {
s0 <- sum(etat[,j+1]%in%(c("00","01","02","03","04","05","06","07","10","11","12","13","14","15","16","17")));
s1 <- sum(etat[,j+1]%in%(c("10","11","12","13","14","15","16","17")));
if (s0 != 0) {
prop_dem_global[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prop_dem_diabet)) {
s0 <- sum(etat[,j+1]%in%(c("02","04","06","07","12","14","16","17")));
s1 <- sum(etat[,j+1]%in%(c("12","14","16","17")));
if (s0 != 0) {
prop_dem_diabet[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prop_dem_hypert)) {
s0 <- sum(etat[,j+1]%in%(c("01","04","05","07","11","14","15","17")));
s1 <- sum(etat[,j+1]%in%(c("11","14","15","17")));
if (s0 != 0) {
prop_dem_hypert[j,age-63] <- s1/s0;
};
}
};
if (age >= 65) {
for (j in 1:nrow(prop_dem_inacti)) {
s0 <- sum(etat[,j+1]%in%(c("03","05","06","07","13","15","16","17")));
s1 <- sum(etat[,j+1]%in%(c("13","15","16","17")));
if (s0 != 0) {
prop_dem_inacti[j,age-63] <- s1/s0;
};
}
};
}
### Computation for variability
if (nb_iter != 0) {
indicateurs <- varHI(t = t,
intervention = intervention,
year_intervention = year_intervention,
nb_people = nb_people,
nb_iter = nb_iter,
data_pop = data_pop,
gender = gender,
data_prev = data_prev,
data_incid = data_incid,
a010 = a010,
a011 = a011,
a01_global = a01_global,
a020 = a020,
a021 = a021,
a02_global = a02_global,
a120 = a120,
a121 = a121,
a12_global = a12_global,
data_theta01 = data_theta01,
data_theta02 = data_theta02,
data_theta12 = data_theta12,
RR = RR,
prb_dem = prb_dem,
age_dem = age_dem,
Ncpus = Ncpus)
for (i in 1:nb_iter) {
esp_vie_gen[,i+2] <- indicateurs[,i]$LE_overall
esp_vie_gen_conso[,i+2] <- indicateurs[,i]$LE_overall_exp
esp_vie_gen_nonconso[,i+2] <- indicateurs[,i]$LE_overall_nonexp
esp_vie_sans_mal[,i+2] <- indicateurs[,i]$LE_without_dis
esp_vie_sans_mal_conso[,i+2] <- indicateurs[,i]$LE_without_dis_exp
esp_vie_sans_mal_nonconso[,i+2] <- indicateurs[,i]$LE_without_dis_nonexp
esp_vie_mal[,i+2] <- indicateurs[,i]$LE_dis
esp_vie_mal_conso[,i+2] <- indicateurs[,i]$LE_dis_exp
esp_vie_mal_nonconso[,i+2] <- indicateurs[,i]$LE_dis_nonexp
esp_vie_non_mal[,i+2] <- indicateurs[,i]$LE_non_dis
esp_vie_non_mal_conso[,i+2] <- indicateurs[,i]$LE_non_dis_exp
esp_vie_non_mal_nonconso[,i+2] <- indicateurs[,i]$LE_non_dis_nonexp
prevalence[,i+2] <- indicateurs[,i]$np_age
taux_prevalence[,i+2] <- indicateurs[,i]$tp_dis
survie[,i+2] <- indicateurs[,i]$nsurvival
taux_survivants[,i+2] <- indicateurs[,i]$rsurvival
nb_moy_dem[,i+2] <- indicateurs[,i]$nb_dis
nb_moy_dem_conso [,i+2] <- indicateurs[,i]$nb_dis_exp
nb_moy_dem_nonconso [,i+2] <- indicateurs[,i]$nb_dis_nonexp
prb_dem[,i+2] <- indicateurs[,i]$p_dis
age_dem[,i+2] <- indicateurs[,i]$a_dis
moy_conso[,i+2] <- indicateurs[,i]$m_exp
prevalence_conso[,i+2] <- indicateurs[,i]$p_exp
quotient_mortalite[,i+2] <- indicateurs[,i]$mortality_r
}
}
### Computation of health indicators :
### Overall life-expectancy
life_expectancy <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy[,1] <- esp_vie_gen[,1]
life_expectancy[,2] <- esp_vie_gen[,2]
life_expectancy[,3] <- apply(esp_vie_gen[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy[,4] <- life_expectancy[,2] - 1.96*life_expectancy[,3];
life_expectancy[,5] <- life_expectancy[,2] + 1.96*life_expectancy[,3];
for (i in 1:nrow(life_expectancy)) {
if (life_expectancy[i,4]<0 & is.na(life_expectancy[i,4])==F) {
life_expectancy[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy)) {
if (life_expectancy[i,2]==0 & life_expectancy[i,4]==0 & life_expectancy[i,5]==0 &
is.na(life_expectancy[i,2])==F & is.na(life_expectancy[i,4])==F & is.na(life_expectancy[i,5])==F) {
life_expectancy[i,2] <- NA;
life_expectancy[i,3] <- NA;
life_expectancy[i,4] <- NA;
life_expectancy[i,5] <- NA
}
}
colnames(life_expectancy) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Overall life-expectancy on exposed peoples
life_expectancy_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_exp[,1] <- esp_vie_gen_conso[,1]
life_expectancy_exp[,2] <- esp_vie_gen_conso[,2]
life_expectancy_exp[,3] <- apply(esp_vie_gen_conso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_exp[,4] <- life_expectancy_exp[,2] - 1.96*life_expectancy_exp[,3];
life_expectancy_exp[,5] <- life_expectancy_exp[,2] + 1.96*life_expectancy_exp[,3];
for (i in 1:nrow(life_expectancy_exp)) {
if (life_expectancy_exp[i,4]<0 & is.na(life_expectancy_exp[i,4])==F) {
life_expectancy_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_exp)) {
if (life_expectancy_exp[i,2]==0 & life_expectancy_exp[i,4]==0 & life_expectancy_exp[i,5]==0 &
is.na(life_expectancy_exp[i,2])==F & is.na(life_expectancy_exp[i,4])==F & is.na(life_expectancy_exp[i,5])==F) {
life_expectancy_exp[i,2] <- NA;
life_expectancy_exp[i,3] <- NA;
life_expectancy_exp[i,4] <- NA;
life_expectancy_exp[i,5] <- NA
}
}
colnames(life_expectancy_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Overall life-expectancy on non exposed peoples
life_expectancy_n_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_n_exp[,1] <- esp_vie_gen_nonconso[,1]
life_expectancy_n_exp[,2] <- esp_vie_gen_nonconso[,2]
life_expectancy_n_exp[,3] <- apply(esp_vie_gen_nonconso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_n_exp[,4] <- life_expectancy_n_exp[,2] - 1.96*life_expectancy_n_exp[,3];
life_expectancy_n_exp[,5] <- life_expectancy_n_exp[,2] + 1.96*life_expectancy_n_exp[,3];
for (i in 1:nrow(life_expectancy_n_exp)) {
if (life_expectancy_n_exp[i,4]<0 & is.na(life_expectancy_n_exp[i,4])==F) {
life_expectancy_n_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_n_exp)) {
if (life_expectancy_n_exp[i,2]==0 & life_expectancy_n_exp[i,4]==0 & life_expectancy_n_exp[i,5]==0 &
is.na(life_expectancy_n_exp[i,2])==F & is.na(life_expectancy_n_exp[i,4])==F & is.na(life_expectancy_n_exp[i,5])==F) {
life_expectancy_n_exp[i,2] <- NA;
life_expectancy_n_exp[i,3] <- NA;
life_expectancy_n_exp[i,4] <- NA;
life_expectancy_n_exp[i,5] <- NA
}
}
colnames(life_expectancy_n_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy without disease
life_expectancy_w_dis <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_w_dis[,1] <- esp_vie_sans_mal[,1]
life_expectancy_w_dis[,2] <- esp_vie_sans_mal[,2]
life_expectancy_w_dis[,3] <- apply(esp_vie_sans_mal[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_w_dis[,4] <- life_expectancy_w_dis[,2] - 1.96*life_expectancy_w_dis[,3];
life_expectancy_w_dis[,5] <- life_expectancy_w_dis[,2] + 1.96*life_expectancy_w_dis[,3];
for (i in 1:nrow(life_expectancy_w_dis)) {
if (life_expectancy_w_dis[i,4]<0 & is.na(life_expectancy_w_dis[i,4])==F) {
life_expectancy_w_dis[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_w_dis)) {
if (life_expectancy_w_dis[i,2]==0 & life_expectancy_w_dis[i,4]==0 & life_expectancy_w_dis[i,5]==0 &
is.na(life_expectancy_w_dis[i,2])==F & is.na(life_expectancy_w_dis[i,4])==F & is.na(life_expectancy_w_dis[i,5])==F) {
life_expectancy_w_dis[i,2] <- NA;
life_expectancy_w_dis[i,3] <- NA;
life_expectancy_w_dis[i,4] <- NA;
life_expectancy_w_dis[i,5] <- NA
}
}
colnames(life_expectancy_w_dis) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy without disease on exposed peoples
life_expectancy_w_dis_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_w_dis_exp[,1] <- esp_vie_sans_mal_conso[,1]
life_expectancy_w_dis_exp[,2] <- esp_vie_sans_mal_conso[,2]
life_expectancy_w_dis_exp[,3] <- apply(esp_vie_sans_mal_conso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_w_dis_exp[,4] <- life_expectancy_w_dis_exp[,2] - 1.96*life_expectancy_w_dis_exp[,3];
life_expectancy_w_dis_exp[,5] <- life_expectancy_w_dis_exp[,2] + 1.96*life_expectancy_w_dis_exp[,3];
for (i in 1:nrow(life_expectancy_w_dis_exp)) {
if (life_expectancy_w_dis_exp[i,4]<0 & is.na(life_expectancy_w_dis_exp[i,4])==F) {
life_expectancy_w_dis_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_w_dis_exp)) {
if (life_expectancy_w_dis_exp[i,2]==0 & life_expectancy_w_dis_exp[i,4]==0 & life_expectancy_w_dis_exp[i,5]==0 &
is.na(life_expectancy_w_dis_exp[i,2])==F & is.na(life_expectancy_w_dis_exp[i,4])==F & is.na(life_expectancy_w_dis_exp[i,5])==F) {
life_expectancy_w_dis_exp[i,2] <- NA;
life_expectancy_w_dis_exp[i,3] <- NA;
life_expectancy_w_dis_exp[i,4] <- NA;
life_expectancy_w_dis_exp[i,5] <- NA
}
}
colnames(life_expectancy_w_dis_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy without disease on non exposed peoples
life_expectancy_w_dis_n_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_w_dis_n_exp[,1] <- esp_vie_sans_mal_nonconso[,1]
life_expectancy_w_dis_n_exp[,2] <- esp_vie_sans_mal_nonconso[,2]
life_expectancy_w_dis_n_exp[,3] <- apply(esp_vie_sans_mal_nonconso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_w_dis_n_exp[,4] <- life_expectancy_w_dis_n_exp[,2] - 1.96*life_expectancy_w_dis_n_exp[,3];
life_expectancy_w_dis_n_exp[,5] <- life_expectancy_w_dis_n_exp[,2] + 1.96*life_expectancy_w_dis_n_exp[,3];
for (i in 1:nrow(life_expectancy_w_dis_n_exp)) {
if (life_expectancy_w_dis_n_exp[i,4]<0 & is.na(life_expectancy_w_dis_n_exp[i,4])==F) {
life_expectancy_w_dis_n_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_w_dis_n_exp)) {
if (life_expectancy_w_dis_n_exp[i,2]==0 & life_expectancy_w_dis_n_exp[i,4]==0 & life_expectancy_w_dis_n_exp[i,5]==0 &
is.na(life_expectancy_w_dis_n_exp[i,2])==F & is.na(life_expectancy_w_dis_n_exp[i,4])==F & is.na(life_expectancy_w_dis_n_exp[i,5])==F) {
life_expectancy_w_dis_n_exp[i,2] <- NA;
life_expectancy_w_dis_n_exp[i,3] <- NA;
life_expectancy_w_dis_n_exp[i,4] <- NA;
life_expectancy_w_dis_n_exp[i,5] <- NA
}
}
colnames(life_expectancy_w_dis_n_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for diseased subject
life_expectancy_dis <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_dis[,1] <- esp_vie_mal[,1]
life_expectancy_dis[,2] <- esp_vie_mal[,2]
life_expectancy_dis[,3] <- apply(esp_vie_mal[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_dis[,4] <- life_expectancy_dis[,2] - 1.96*life_expectancy_dis[,3];
life_expectancy_dis[,5] <- life_expectancy_dis[,2] + 1.96*life_expectancy_dis[,3];
for (i in 1:nrow(life_expectancy_dis)) {
if (life_expectancy_dis[i,4]<0 & is.na(life_expectancy_dis[i,4])==F) {
life_expectancy_dis[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_dis)) {
if (life_expectancy_dis[i,2]==0 & life_expectancy_dis[i,4]==0 & life_expectancy_dis[i,5]==0 &
is.na(life_expectancy_dis[i,2])==F & is.na(life_expectancy_dis[i,4])==F & is.na(life_expectancy_dis[i,5])==F) {
life_expectancy_dis[i,2] <- NA;
life_expectancy_dis[i,3] <- NA;
life_expectancy_dis[i,4] <- NA;
life_expectancy_dis[i,5] <- NA
}
}
colnames(life_expectancy_dis) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for diseased subject on exposed peoples
life_expectancy_dis_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_dis_exp[,1] <- esp_vie_mal_conso[,1]
life_expectancy_dis_exp[,2] <- esp_vie_mal_conso[,2]
life_expectancy_dis_exp[,3] <- apply(esp_vie_mal_conso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_dis_exp[,4] <- life_expectancy_dis_exp[,2] - 1.96*life_expectancy_dis_exp[,3];
life_expectancy_dis_exp[,5] <- life_expectancy_dis_exp[,2] + 1.96*life_expectancy_dis_exp[,3];
for (i in 1:nrow(life_expectancy_dis_exp)) {
if (life_expectancy_dis_exp[i,4]<0 & is.na(life_expectancy_dis_exp[i,4])==F) {
life_expectancy_dis_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_dis_exp)) {
if (life_expectancy_dis_exp[i,2]==0 & life_expectancy_dis_exp[i,4]==0 & life_expectancy_dis_exp[i,5]==0 &
is.na(life_expectancy_dis_exp[i,2])==F & is.na(life_expectancy_dis_exp[i,4])==F & is.na(life_expectancy_dis_exp[i,5])==F) {
life_expectancy_dis_exp[i,2] <- NA;
life_expectancy_dis_exp[i,3] <- NA;
life_expectancy_dis_exp[i,4] <- NA;
life_expectancy_dis_exp[i,5] <- NA
}
}
colnames(life_expectancy_dis_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for diseased subject on non exposed peoples
life_expectancy_dis_n_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_dis_n_exp[,1] <- esp_vie_mal_nonconso[,1]
life_expectancy_dis_n_exp[,2] <- esp_vie_mal_nonconso[,2]
life_expectancy_dis_n_exp[,3] <- apply(esp_vie_mal_nonconso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_dis_n_exp[,4] <- life_expectancy_dis_n_exp[,2] - 1.96*life_expectancy_dis_n_exp[,3];
life_expectancy_dis_n_exp[,5] <- life_expectancy_dis_n_exp[,2] + 1.96*life_expectancy_dis_n_exp[,3];
for (i in 1:nrow(life_expectancy_dis_n_exp)) {
if (life_expectancy_dis_n_exp[i,4]<0 & is.na(life_expectancy_dis_n_exp[i,4])==F) {
life_expectancy_dis_n_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_dis_n_exp)) {
if (life_expectancy_dis_n_exp[i,2]==0 & life_expectancy_dis_n_exp[i,4]==0 & life_expectancy_dis_n_exp[i,5]==0 &
is.na(life_expectancy_dis_n_exp[i,2])==F & is.na(life_expectancy_dis_n_exp[i,4])==F & is.na(life_expectancy_dis_n_exp[i,5])==F) {
life_expectancy_dis_n_exp[i,2] <- NA;
life_expectancy_dis_n_exp[i,3] <- NA;
life_expectancy_dis_n_exp[i,4] <- NA;
life_expectancy_dis_n_exp[i,5] <- NA
}
}
colnames(life_expectancy_dis_n_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for non diseased subject
life_expectancy_n_dis <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_n_dis[,1] <- esp_vie_non_mal[,1]
life_expectancy_n_dis[,2] <- esp_vie_non_mal[,2]
life_expectancy_n_dis[,3] <- apply(esp_vie_non_mal[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_n_dis[,4] <- life_expectancy_n_dis[,2] - 1.96*life_expectancy_n_dis[,3];
life_expectancy_n_dis[,5] <- life_expectancy_n_dis[,2] + 1.96*life_expectancy_n_dis[,3];
for (i in 1:nrow(life_expectancy_n_dis)) {
if (life_expectancy_n_dis[i,4]<0 & is.na(life_expectancy_n_dis[i,4])==F) {
life_expectancy_n_dis[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_n_dis)) {
if (life_expectancy_n_dis[i,2]==0 & life_expectancy_n_dis[i,4]==0 & life_expectancy_n_dis[i,5]==0 &
is.na(life_expectancy_n_dis[i,2])==F & is.na(life_expectancy_n_dis[i,4])==F & is.na(life_expectancy_n_dis[i,5])==F) {
life_expectancy_n_dis[i,2] <- NA;
life_expectancy_n_dis[i,3] <- NA;
life_expectancy_n_dis[i,4] <- NA;
life_expectancy_n_dis[i,5] <- NA
}
}
colnames(life_expectancy_n_dis) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for non diseased subject on exposed peoples
life_expectancy_n_dis_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_n_dis_exp[,1] <- esp_vie_non_mal_conso[,1]
life_expectancy_n_dis_exp[,2] <- esp_vie_non_mal_conso[,2]
life_expectancy_n_dis_exp[,3] <- apply(esp_vie_non_mal_conso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_n_dis_exp[,4] <- life_expectancy_n_dis_exp[,2] - 1.96*life_expectancy_n_dis_exp[,3];
life_expectancy_n_dis_exp[,5] <- life_expectancy_n_dis_exp[,2] + 1.96*life_expectancy_n_dis_exp[,3];
for (i in 1:nrow(life_expectancy_n_dis_exp)) {
if (life_expectancy_n_dis_exp[i,4]<0 & is.na(life_expectancy_n_dis_exp[i,4])==F) {
life_expectancy_n_dis_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_n_dis_exp)) {
if (life_expectancy_n_dis_exp[i,2]==0 & life_expectancy_n_dis_exp[i,4]==0 & life_expectancy_n_dis_exp[i,5]==0 &
is.na(life_expectancy_n_dis_exp[i,2])==F & is.na(life_expectancy_n_dis_exp[i,4])==F & is.na(life_expectancy_n_dis_exp[i,5])==F) {
life_expectancy_n_dis_exp[i,2] <- NA;
life_expectancy_n_dis_exp[i,3] <- NA;
life_expectancy_n_dis_exp[i,4] <- NA;
life_expectancy_n_dis_exp[i,5] <- NA
}
}
colnames(life_expectancy_n_dis_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Life-expectancy for non diseased subject on non exposed peoples
life_expectancy_n_dis_n_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
life_expectancy_n_dis_n_exp[,1] <- esp_vie_non_mal_nonconso[,1]
life_expectancy_n_dis_n_exp[,2] <- esp_vie_non_mal_nonconso[,2]
life_expectancy_n_dis_n_exp[,3] <- apply(esp_vie_non_mal_nonconso[,-c(1:2)], 1, sd, na.rm=T);
life_expectancy_n_dis_n_exp[,4] <- life_expectancy_n_dis_n_exp[,2] - 1.96*life_expectancy_n_dis_n_exp[,3];
life_expectancy_n_dis_n_exp[,5] <- life_expectancy_n_dis_n_exp[,2] + 1.96*life_expectancy_n_dis_n_exp[,3];
for (i in 1:nrow(life_expectancy_n_dis_n_exp)) {
if (life_expectancy_n_dis_n_exp[i,4]<0 & is.na(life_expectancy_n_dis_n_exp[i,4])==F) {
life_expectancy_n_dis_n_exp[i,4] <- 0
}
}
for (i in 1:nrow(life_expectancy_n_dis_n_exp)) {
if (life_expectancy_n_dis_n_exp[i,2]==0 & life_expectancy_n_dis_n_exp[i,4]==0 & life_expectancy_n_dis_n_exp[i,5]==0 &
is.na(life_expectancy_n_dis_n_exp[i,2])==F & is.na(life_expectancy_n_dis_n_exp[i,4])==F & is.na(life_expectancy_n_dis_n_exp[i,5])==F) {
life_expectancy_n_dis_n_exp[i,2] <- NA;
life_expectancy_n_dis_n_exp[i,3] <- NA;
life_expectancy_n_dis_n_exp[i,4] <- NA;
life_expectancy_n_dis_n_exp[i,5] <- NA
}
}
colnames(life_expectancy_n_dis_n_exp) <- c("age","life-expectancy","std","CI95_low","CI95_upp");
### Prevalence of disease
prevalence <- prevalence[-1,]
if (nb_iter != 0) {
prev <- colSums(prevalence[,-1])
} else {
prev <- sum(prevalence[,-1])
}
number_prevalence <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
number_prevalence[1] <- as.integer(t);
number_prevalence[2] <- prev[1];
number_prevalence[3] <- sd(prev[-1], na.rm=T);
number_prevalence[4] <- number_prevalence[2] - 1.96*number_prevalence[3];
number_prevalence[5] <- number_prevalence[2] + 1.96*number_prevalence[3];
if (number_prevalence[4]<0 & is.na(number_prevalence[4])==F) {
number_prevalence[4] <- 0
};
if (number_prevalence[2]==0 & number_prevalence[4]==0 & number_prevalence[5]==0 &
is.na(number_prevalence[2])==F & is.na(number_prevalence[4])==F & is.na(number_prevalence[5])==F) {
number_prevalence[2] <- NA;
number_prevalence[3] <- NA;
number_prevalence[4] <- NA;
number_prevalence[5] <- NA
};
colnames(number_prevalence) <- c("year","prevalence","std","CI95_low","CI95_upp");
number_prevalence <- number_prevalence[,-1];
### Prevalence of disease by age
number_prev_age <- matrix(c(0),
nrow=99-66+1,
ncol=5,
byrow = T);
number_prev_age[,1] <- prevalence[,1]
number_prev_age[,2] <- prevalence[,2]
number_prev_age[,3] <- apply(prevalence[,-c(1:2)], 1, sd, na.rm=T);
number_prev_age[,4] <- number_prev_age[,2] - 1.96*number_prev_age[,3];
number_prev_age[,5] <- number_prev_age[,2] + 1.96*number_prev_age[,3];
for (i in 1:nrow(number_prev_age)) {
if (number_prev_age[i,4]<0 & is.na(number_prev_age[i,4])==F) {
number_prev_age[i,4] <- 0
}
}
for (i in 1:nrow(number_prev_age)) {
if (number_prev_age[i,2]==0 & number_prev_age[i,4]==0 & number_prev_age[i,5]==0 &
is.na(number_prev_age[i,2])==F & is.na(number_prev_age[i,4])==F & is.na(number_prev_age[i,5])==F) {
number_prev_age[i,2] <- NA;
number_prev_age[i,3] <- NA;
number_prev_age[i,4] <- NA;
number_prev_age[i,5] <- NA
}
}
colnames(number_prev_age) <- c("age","prevalence","std","CI95_low","CI95_upp");
### Prevalence rate of disease by age
taux_prevalence <- taux_prevalence[-1,]
prev_rate_disease_age <- matrix(c(0),
nrow=99-66+1,
ncol=5,
byrow = T);
prev_rate_disease_age[,1] <- taux_prevalence[,1]
prev_rate_disease_age[,2] <- taux_prevalence[,2]
prev_rate_disease_age[,3] <- apply(taux_prevalence[,-c(1:2)], 1, sd, na.rm=T);
prev_rate_disease_age[,4] <- prev_rate_disease_age[,2] - 1.96*prev_rate_disease_age[,3];
prev_rate_disease_age[,5] <- prev_rate_disease_age[,2] + 1.96*prev_rate_disease_age[,3];
for (i in 1:nrow(prev_rate_disease_age)) {
if (prev_rate_disease_age[i,4]<0 & is.na(prev_rate_disease_age[i,4])==F) {
prev_rate_disease_age[i,4] <- 0
}
}
for (i in 1:nrow(prev_rate_disease_age)) {
if (prev_rate_disease_age[i,5]>1 & is.na(prev_rate_disease_age[i,5])==F) {
prev_rate_disease_age[i,5] <- 1
}
}
for (i in 1:nrow(prev_rate_disease_age)) {
if (prev_rate_disease_age[i,2]==0 & prev_rate_disease_age[i,4]==0 & prev_rate_disease_age[i,5]==0 &
is.na(prev_rate_disease_age[i,2])==F & is.na(prev_rate_disease_age[i,4])==F & is.na(prev_rate_disease_age[i,5])==F) {
prev_rate_disease_age[i,2] <- NA;
prev_rate_disease_age[i,3] <- NA;
prev_rate_disease_age[i,4] <- NA;
prev_rate_disease_age[i,5] <- NA
}
}
colnames(prev_rate_disease_age) <- c("age","prevalence_rate","std","CI95_low","CI95_upp");
### Survival
if (nb_iter != 0) {
surv <- colSums(survie[,-1])
} else {
surv <- sum(survie[,-1])
}
number_survival <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
number_survival[1] <- as.integer(t);
number_survival[2] <- surv[1]
number_survival[3] <- sd(surv[-1], na.rm=T);
number_survival[4] <- number_survival[2] - 1.96*number_survival[3];
number_survival[5] <- number_survival[2] + 1.96*number_survival[3];
if (number_survival[4]<0 & is.na(number_survival[4])==F) {
number_survival[4] <- 0
};
if (number_survival[2]==0 & number_survival[4]==0 & number_survival[5]==0 &
is.na(number_survival[2])==F & is.na(number_survival[4])==F & is.na(number_survival[5])==F) {
number_survival[2] <- NA;
number_survival[3] <- NA;
number_survival[4] <- NA;
number_survival[5] <- NA
};
colnames(number_survival) <- c("year","survival","std","CI95_low","CI95_upp");
number_survival <- number_survival[,-1];
### Survival by age
number_survival_age <- matrix(c(0),
nrow=99-65+1,
ncol=5,
byrow = T);
number_survival_age[,1] <- survie[,1]
number_survival_age[,2] <- survie[,2]
number_survival_age[,3] <- apply(survie[,-c(1:2)], 1, sd, na.rm=T);
number_survival_age[,4] <- number_survival_age[,2] - 1.96*number_survival_age[,3];
number_survival_age[,5] <- number_survival_age[,2] + 1.96*number_survival_age[,3];
for (i in 1:nrow(number_survival_age)) {
if (number_survival_age[i,4]<0 & is.na(number_survival_age[i,4])==F) {
number_survival_age[i,4] <- 0
}
}
for (i in 1:nrow(number_survival_age)) {
if (number_survival_age[i,2]==0 & number_survival_age[i,4]==0 & number_survival_age[i,5]==0 &
is.na(number_survival_age[i,2])==F & is.na(number_survival_age[i,4])==F & is.na(number_survival_age[i,5])==F) {
number_survival_age[i,2] <- NA;
number_survival_age[i,3] <- NA;
number_survival_age[i,4] <- NA;
number_survival_age[i,5] <- NA
}
}
colnames(number_survival_age) <- c("age","survival","std","CI95_low","CI95_upp");
### Survival rate
taux_survivants <- taux_survivants[-1,]
survival_rate <- matrix(c(0),
nrow=99-66+1,
ncol=5,
byrow = T);
survival_rate[,1] <- taux_survivants[,1]
survival_rate[,2] <- taux_survivants[,2]
survival_rate[,3] <- apply(taux_survivants[,-c(1:2)], 1, sd, na.rm=T);
survival_rate[,4] <- survival_rate[,2] - 1.96*survival_rate[,3];
survival_rate[,5] <- survival_rate[,2] + 1.96*survival_rate[,3];
for (i in 1:nrow(survival_rate)) {
if (survival_rate[i,4]<0 & is.na(survival_rate[i,4])==F) {
survival_rate[i,4] <- 0
}
}
for (i in 1:nrow(survival_rate)) {
if (survival_rate[i,5]>1 & is.na(survival_rate[i,5])==F) {
survival_rate[i,5] <- 1
}
}
for (i in 1:nrow(survival_rate)) {
if (survival_rate[i,2]==0 & survival_rate[i,4]==0 & survival_rate[i,5]==0 &
is.na(survival_rate[i,2])==F & is.na(survival_rate[i,4])==F & is.na(survival_rate[i,5])==F) {
survival_rate[i,2] <- NA;
survival_rate[i,3] <- NA;
survival_rate[i,4] <- NA;
survival_rate[i,5] <- NA
}
}
colnames(survival_rate) <- c("age","survival_rate","std","CI95_low","CI95_upp");
### Global prevalence rate of disease
taux_prev_demence <- matrix(c(0),
nrow=1,
ncol=length(prev)+1,
byrow = T);
taux_prev_demence[1] <- t;
for (i in 2:ncol(taux_prev_demence)) {
taux_prev_demence[1,i] <- prev[i-1] / surv[i-1];
};
prev_rate_disease <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
prev_rate_disease[1] <- as.integer(t);
prev_rate_disease[2] <- taux_prev_demence[,2];
prev_rate_disease[3] <- sd(taux_prev_demence[,-c(1:2)], na.rm=T);
prev_rate_disease[4] <- prev_rate_disease[2] - 1.96*prev_rate_disease[3];
prev_rate_disease[5] <- prev_rate_disease[2] + 1.96*prev_rate_disease[3];
if (prev_rate_disease[4]<0 & is.na(prev_rate_disease[4])==F) {
prev_rate_disease[4] <- 0
};
if (prev_rate_disease[2]==0 & prev_rate_disease[4]==0 & prev_rate_disease[5]==0 &
is.na(prev_rate_disease[2])==F & is.na(prev_rate_disease[4])==F & is.na(prev_rate_disease[5])==F) {
prev_rate_disease[2] <- NA;
prev_rate_disease[3] <- NA;
prev_rate_disease[4] <- NA;
prev_rate_disease[5] <- NA
};
colnames(prev_rate_disease) <- c("year","prevalence_rate","std","CI95_low","CI95_upp");
prev_rate_disease <- prev_rate_disease[,-1];
### Mean number of years spent with disease
number_years_disease <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
number_years_disease[,1] <- nb_moy_dem[,1]
number_years_disease[,2] <- nb_moy_dem[,2]
number_years_disease[,3] <- apply(nb_moy_dem[,-c(1:2)], 1, sd, na.rm=T);
number_years_disease[,4] <- number_years_disease[,2] - 1.96*number_years_disease[,3];
number_years_disease[,5] <- number_years_disease[,2] + 1.96*number_years_disease[,3];
for (i in 1:nrow(number_years_disease)) {
if (number_years_disease[i,4]<0 & is.na(number_years_disease[i,4])==F) {
number_years_disease[i,4] <- 0
}
}
for (i in 1:nrow(number_years_disease)) {
if (number_years_disease[i,2]==0 & number_years_disease[i,4]==0 & number_years_disease[i,5]==0 &
is.na(number_years_disease[i,2])==F & is.na(number_years_disease[i,4])==F & is.na(number_years_disease[i,5])==F) {
number_years_disease[i,2] <- NA;
number_years_disease[i,3] <- NA;
number_years_disease[i,4] <- NA;
number_years_disease[i,5] <- NA
}
}
colnames(number_years_disease) <- c("age","number_years","std","CI95_low","CI95_upp");
### Mean number of years spent with disease on exposed peoples
number_years_disease_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
number_years_disease_exp[,1] <- nb_moy_dem_conso[,1]
number_years_disease_exp[,2] <- nb_moy_dem_conso[,2]
number_years_disease_exp[,3] <- apply(nb_moy_dem_conso[,-c(1:2)], 1, sd, na.rm=T);
number_years_disease_exp[,4] <- number_years_disease_exp[,2] - 1.96*number_years_disease_exp[,3];
number_years_disease_exp[,5] <- number_years_disease_exp[,2] + 1.96*number_years_disease_exp[,3];
for (i in 1:nrow(number_years_disease_exp)) {
if (number_years_disease_exp[i,4]<0 & is.na(number_years_disease_exp[i,4])==F) {
number_years_disease_exp[i,4] <- 0
}
}
for (i in 1:nrow(number_years_disease_exp)) {
if (number_years_disease_exp[i,2]==0 & number_years_disease_exp[i,4]==0 & number_years_disease_exp[i,5]==0 &
is.na(number_years_disease_exp[i,2])==F & is.na(number_years_disease_exp[i,4])==F & is.na(number_years_disease_exp[i,5])==F) {
number_years_disease_exp[i,2] <- NA;
number_years_disease_exp[i,3] <- NA;
number_years_disease_exp[i,4] <- NA;
number_years_disease_exp[i,5] <- NA
}
}
colnames(number_years_disease_exp) <- c("age","number_years","std","CI95_low","CI95_upp");
### Mean number of years spent with disease on non exposed peoples
number_years_disease_n_exp <- matrix(c(0),
nrow=100-65+1,
ncol=5,
byrow = T);
number_years_disease_n_exp[,1] <- nb_moy_dem_nonconso[,1]
number_years_disease_n_exp[,2] <- nb_moy_dem_nonconso[,2]
number_years_disease_n_exp[,3] <- apply(nb_moy_dem_nonconso[,-c(1:2)], 1, sd, na.rm=T);
number_years_disease_n_exp[,4] <- number_years_disease_n_exp[,2] - 1.96*number_years_disease_n_exp[,3];
number_years_disease_n_exp[,5] <- number_years_disease_n_exp[,2] + 1.96*number_years_disease_n_exp[,3];
for (i in 1:nrow(number_years_disease_n_exp)) {
if (number_years_disease_n_exp[i,4]<0 & is.na(number_years_disease_n_exp[i,4])==F) {
number_years_disease_n_exp[i,4] <- 0
}
}
for (i in 1:nrow(number_years_disease_n_exp)) {
if (number_years_disease_n_exp[i,2]==0 & number_years_disease_n_exp[i,4]==0 & number_years_disease_n_exp[i,5]==0 &
is.na(number_years_disease_n_exp[i,2])==F & is.na(number_years_disease_n_exp[i,4])==F & is.na(number_years_disease_n_exp[i,5])==F) {
number_years_disease_n_exp[i,2] <- NA;
number_years_disease_n_exp[i,3] <- NA;
number_years_disease_n_exp[i,4] <- NA;
number_years_disease_n_exp[i,5] <- NA
}
}
colnames(number_years_disease_n_exp) <- c("age","number_years","std","CI95_low","CI95_upp");
### Life-long probability of disease
prb_dem <- prb_dem[-1,]
prb_demence <- matrix(c(0),
nrow=1,
ncol=ncol(prb_dem),
byrow = T);
prb_demence[1] <- t;
for (i in 2:ncol(prb_dem)) {
prb_demence[i] <- sum(prb_dem[,i]) / nrow(etat);
};
ll_prob_disease <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
ll_prob_disease[1] <- as.integer(t);
ll_prob_disease[2] <- prb_demence[,2];
ll_prob_disease[3] <- sd(prb_demence[,-c(1:2)], na.rm=T);
ll_prob_disease[4] <- ll_prob_disease[2] - 1.96*ll_prob_disease[3];
ll_prob_disease[5] <- ll_prob_disease[2] + 1.96*ll_prob_disease[3];
if (ll_prob_disease[4]<0 & is.na(ll_prob_disease[4])==F) {
ll_prob_disease[4] <- 0
};
if (ll_prob_disease[2]==0 & ll_prob_disease[4]==0 & ll_prob_disease[5]==0 &
is.na(ll_prob_disease[2])==F & is.na(ll_prob_disease[4])==F & is.na(ll_prob_disease[5])==F) {
ll_prob_disease[2] <- NA;
ll_prob_disease[3] <- NA;
ll_prob_disease[4] <- NA;
ll_prob_disease[5] <- NA
};
colnames(ll_prob_disease) <- c("year","probability","std","CI95_low","CI95_upp");
ll_prob_disease <- ll_prob_disease[,-1];
### Average age at disease onset
age_dem <- age_dem[-1,]
age_demence <- matrix(c(0),
nrow=1,
ncol=ncol(age_dem),
byrow = T);
age_demence[1] <- t;
n0 <- vector(length = nrow(age_dem));
for (i in 2:ncol(age_dem)) {
for (j in 1:nrow(age_dem)) {
n0[j] <- age_dem[j,1]*age_dem[j,i]
};
age_demence[i] <- sum(n0)/sum(age_dem[,i]);
}
average_age_disease <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
average_age_disease[1] <- as.integer(t);
average_age_disease[2] <- age_demence[,2]
average_age_disease[3] <- sd(age_demence[,-c(1:2)], na.rm=T);
average_age_disease[4] <- average_age_disease[2] - 1.96*average_age_disease[3];
average_age_disease[5] <- average_age_disease[2] + 1.96*average_age_disease[3];
if (average_age_disease[4]<0 & is.na(average_age_disease[4])==F) {
average_age_disease[4] <- 0
};
if (average_age_disease[2]==0 & average_age_disease[4]==0 & average_age_disease[5]==0 &
is.na(average_age_disease[2])==F & is.na(average_age_disease[4])==F & is.na(average_age_disease[5])==F) {
average_age_disease[2] <- NA;
average_age_disease[3] <- NA;
average_age_disease[4] <- NA;
average_age_disease[5] <- NA
};
colnames(average_age_disease) <- c("year","mean_age","std","CI95_low","CI95_upp");
average_age_disease <- average_age_disease[,-1];
### Mean number of years of exposition
moy_conso <- moy_conso[-1,]
moyenne_conso <- matrix(c(0),
nrow=1,
ncol=ncol(moy_conso),
byrow = T);
moyenne_conso[1] <- t;
for (i in 2:ncol(moy_conso)) {
moyenne_conso[i] <- sum(moy_conso[,i]) / nrow(etat[which(etat[,1]%in%("00") | etat[,1]%in%("10")),]); # nombre moyen d'années de consommation de benzo
}
number_years_exp <- matrix(c(0),
nrow=1,
ncol=5,
byrow = T);
number_years_exp[1] <- as.integer(t);
number_years_exp[2] <- moyenne_conso[,2]
number_years_exp[3] <- sd(moyenne_conso[,-c(1:2)], na.rm=T);
number_years_exp[4] <- number_years_exp[2] - 1.96*number_years_exp[3];
number_years_exp[5] <- number_years_exp[2] + 1.96*number_years_exp[3];
if (number_years_exp[4]<0 & is.na(number_years_exp[4])==F) {
number_years_exp[4] <- 0
};
if (number_years_exp[2]==0 & number_years_exp[4]==0 & number_years_exp[5]==0 &
is.na(number_years_exp[2])==F & is.na(number_years_exp[4])==F & is.na(number_years_exp[5])==F) {
number_years_exp[2] <- NA;
number_years_exp[3] <- NA;
number_years_exp[4] <- NA;
number_years_exp[5] <- NA
};
colnames(number_years_exp) <- c("year","mean_number","std","CI95_low","CI95_upp");
number_years_exp <- number_years_exp[,-1];
### Number of exposed peoples at least one time
exposition <- matrix(c(0),
nrow=105-65+1,
ncol=5,
byrow = T);
exposition[,1] <- prevalence_conso[,1]
exposition[,2] <- prevalence_conso[,2]
exposition[,3] <- apply(prevalence_conso[,-c(1:2)], 1, sd, na.rm=T);
exposition[,4] <- exposition[,2] - 1.96*exposition[,3];
exposition[,5] <- exposition[,2] + 1.96*exposition[,3];
for (i in 1:nrow(exposition)) {
if (exposition[i,4]<0 & is.na(exposition[i,4])==F) {
exposition[i,4] <- 0
}
}
for (i in 1:nrow(exposition)) {
if (exposition[i,5]>1 & is.na(exposition[i,5])==F) {
exposition[i,5] <- 1
}
}
for (i in 1:nrow(exposition)) {
if (exposition[i,2]==0 & exposition[i,4]==0 & exposition[i,5]==0 &
is.na(exposition[i,2])==F & is.na(exposition[i,4])==F & is.na(exposition[i,5])==F) {
exposition[i,2] <- NA;
exposition[i,3] <- NA;
exposition[i,4] <- NA;
exposition[i,5] <- NA
}
}
colnames(exposition) <- c("age","exposition","std","CI95_low","CI95_upp");
### Mortality rate
quotient_mortalite <- quotient_mortalite[-1,]
mortality_rate <- matrix(c(0),
nrow=99-66+1,
ncol=5,
byrow = T);
mortality_rate[,1] <- quotient_mortalite[,1]
mortality_rate[,2] <- quotient_mortalite[,2]
mortality_rate[,3] <- apply(quotient_mortalite[,-c(1:2)], 1, sd, na.rm=T);
mortality_rate[,4] <- mortality_rate[,2] - 1.96*mortality_rate[,3];
mortality_rate[,5] <- mortality_rate[,2] + 1.96*mortality_rate[,3];
for (i in 1:nrow(mortality_rate)) {
if (mortality_rate[i,4]<0 & is.na(mortality_rate[i,4])==F) {
mortality_rate[i,4] <- 0
}
}
for (i in 1:nrow(mortality_rate)) {
if (mortality_rate[i,5]>1 & is.na(mortality_rate[i,5])==F) {
mortality_rate[i,5] <- 1
}
}
for (i in 1:nrow(mortality_rate)) {
if (mortality_rate[i,2]==0 & mortality_rate[i,4]==0 & mortality_rate[i,5]==0 &
is.na(mortality_rate[i,2])==F & is.na(mortality_rate[i,4])==F & is.na(mortality_rate[i,5])==F) {
mortality_rate[i,2] <- NA;
mortality_rate[i,3] <- NA;
mortality_rate[i,4] <- NA;
mortality_rate[i,5] <- NA
}
}
colnames(mortality_rate) <- c("age","mortality_rate","std","CI95_low","CI95_upp");
### Output of the algorithm
### Overall life-expectancy
list_overall_LE <- list(life_expectancy, life_expectancy_exp, life_expectancy_n_exp)
names(list_overall_LE) <- c("life_expectancy", "life_expectancy_exp", "life_expectancy_n_exp")
### Life-expectancy without disease
list_LE_without_disease <- list(life_expectancy_w_dis, life_expectancy_w_dis_exp, life_expectancy_w_dis_n_exp)
names(list_LE_without_disease) <- c("life_expectancy_w_dis", "life_expectancy_w_dis_exp", "life_expectancy_w_dis_n_exp")
### Life-expectancy for diseased subject
list_LE_diseased <- list(life_expectancy_dis, life_expectancy_dis_exp, life_expectancy_dis_n_exp)
names(list_LE_diseased) <- c("life_expectancy_dis", "life_expectancy_dis_exp", "life_expectancy_dis_n_exp")
### Life-expectancy for non-diseased subject
list_LE_non_diseased <- list(life_expectancy_n_dis, life_expectancy_n_dis_exp, life_expectancy_n_dis_n_exp)
names(list_LE_non_diseased) <- c("life_expectancy_n_dis", "life_expectancy_n_dis_exp", "life_expectancy_n_dis_n_exp")
### Prevalence of disease
list_prevalence_disease <- list(number_prev_age, number_prevalence, prev_rate_disease_age, prev_rate_disease)
names(list_prevalence_disease) <- c("number_prev_age", "number_prevalence", "prev_rate_disease_age", "prev_rate_disease")
### Survival
list_survival <- list(number_survival_age, number_survival, survival_rate)
names(list_survival) <- c("number_survival_age", "number_survival", "survival_rate")
### Mean number of years spent with disease
list_number_years_disease <- list(number_years_disease, number_years_disease_exp, number_years_disease_n_exp)
names(list_number_years_disease) <- c("number_years_disease", "number_years_disease_exp", "number_years_disease_n_exp")
### Summary of all iterations
list_summary_iterations <- list(esp_vie_sans_mal, prevalence, taux_prevalence, prb_dem, age_dem, nb_moy_dem, prev, surv)
names(list_summary_iterations) <- c("esp_vie_sans_mal", "prevalence", "taux_prevalence", "prb_dem", "age_dem", "nb_moy_dem", "prev", "surv")
### Output list
# With exposition
#HI_output <- list(list_overall_LE, list_LE_without_disease, list_LE_diseased, list_LE_non_diseased, list_prevalence_disease, list_survival, list_number_years_disease, ll_prob_disease, average_age_disease, number_years_exp, exposition, mortality_rate);
#names(HI_output) <- c(list_overall_LE", "list_LE_without_disease", "list_LE_diseased", "list_LE_non_diseased", "list_prevalence_disease", "list_survival", "list_number_years_disease", "ll_prob_disease", "average_age_disease", "number_years_exp", "exposition", "mortality_rate");
# Without exposition
HI_output <- list(list_overall_LE, list_LE_without_disease, list_LE_diseased, list_LE_non_diseased, list_prevalence_disease, list_survival, list_number_years_disease, list_summary_iterations, ll_prob_disease, average_age_disease, number_years_exp, mortality_rate);
names(HI_output) <- c("list_overall_LE", "list_LE_without_disease", "list_LE_diseased", "list_LE_non_diseased", "list_prevalence_disease", "list_survival", "list_number_years_disease", "list_summary_iterations", "ll_prob_disease", "average_age_disease", "number_years_exp", "mortality_rate");
return(HI_output)
}
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Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.