individual-heterogeneity: Modeling the Prevalence and the Force of Infection Directly...

In TeaKov/serostat: Modeling Infectious Disease Parameters Based on Serological and Social Contact Data R Package

Description

Individual heterogeneity has been shown to be a key factor in the estimation of the basic reproduction number. Individuals are dissimilar in the way they acquire infections. Some individuals are more susceptible than others and will experience infection earlier.

Usage

GF(PP, PN, NP, NN, a, alpha1eta, beta1eta,
  gamma1eta, alpha2eta, beta2eta, gamma2eta, thetaeta)

Arguments

PP	A numeric vector containing the number of seropositives for both diseases.
PN	A numeric vector containing the number of individuals that are seropositive for the first disease and seronegative for the second disease.
NP	A numeric vector containing the number of individuals that are seropositive for the second disease and seronegative for the first disease.
NN	A numeric vector containing the number of seronegatives for both diseases.
a	A numeric vector containing age.
alpha1eta	Nonnegative scaling parameter for first disease.
beta1eta	Nonnegative scaling parameter for first disease.
gamma1eta	Long-term residual value of the force of infection for the first disease (nonnegative).
alpha2eta	Nonnegative scaling parameter for second disease.
beta2eta	Nonnegative scaling parameter for second disease.
gamma2eta	Long-term residual value of the force of infection for the second disease (nonnegative).
thetaeta	The shape parameter of the gamma frailty distribution.

Details

See page 186 of the book.

See Also

vgam

Examples

# Load Belgian B19 data
library(stats4)
data("VZV_B19_BE_0103")
VZV_B19_BE_0103 <- VZV_B19_BE_0103[!is.na(VZV_B19_BE_0103$VZVres)&
  !is.na(VZV_B19_BE_0103$parvores)&!is.na(VZV_B19_BE_0103$age)&
  VZV_B19_BE_0103$age<70&VZV_B19_BE_0103$age>=1,]
VZV_B19_BE_0103 <- VZV_B19_BE_0103[order(VZV_B19_BE_0103$age),]

y1 <- VZV_B19_BE_0103$VZVres
y2 <- VZV_B19_BE_0103$parvores
age <- VZV_B19_BE_0103$age

a <- unique(age)
covariate <- seq(min(age),max(age),1)

# Counts per age-value
PP <- as.vector(hist(age[y1=="1"&y2=="1"], plot=FALSE, breaks=c(0,a))$counts)
PN <- as.vector(hist(age[y1=="1"&y2=="0"], plot=FALSE, breaks=c(0,a))$counts)
NP <- as.vector(hist(age[y1=="0"&y2=="1"], plot=FALSE, breaks=c(0,a))$counts)
NN <- as.vector(hist(age[y1=="0"&y2=="0"], plot=FALSE, breaks=c(0,a))$counts)

fit <- mle(GF, start=list(alpha1eta=-1, beta1eta=-1,
  gamma1eta=-1, alpha2eta=-1, beta2eta=-1, gamma2eta=-1, thetaeta=-1),
  fixed=list(PP=PP, PN=PN, NP=NP, NN=NN, a=a))
summary(fit)

TeaKov/serostat documentation built on May 21, 2019, 1:21 p.m.