misc/scripts/covidage_v16.5.R
In ocelhay/como: A Web Application of the CoMo COVID-19 Model
require("deSolve")
library("ggplot2")
library("dplyr")
library("reshape2")
require(gridExtra)
library(ggpubr)
library(bsplus)
library(deSolve)
library(DT)
library(highcharter)
library(lubridate)
library(pushbar)
library(readxl)
library(reshape2)
library(scales)
library(shiny)
library(shinyBS)
library(shinycssloaders)
library(shinyhelper)
library(shinythemes)
library(shinyWidgets)
library(tidyverse)
library(XLConnect)
library(stringr)
library(RColorBrewer)
# library("comoOdeCpp")
#read data from excel file
setwd("C:/covid19/covid_age")
load("data_CoMo.RData")
file_path <- paste0(getwd(),"/Template_CoMoCOVID-19App.xlsx")
country_name<-"United Kingdom of Great Britain"
fit_mat <- read.table("fit_mat.txt",header = T)
# Cases
dta <- read_excel(file_path, sheet = "Cases")
names(dta) <- c("date", "cases", "deaths","rep_cases","rep_deaths")
cases_rv <- dta %>%
mutate(date = as.Date(date), cumulative_death = cumsum(deaths)) %>%
as.data.frame()
# Severity/Mortality
dta <- read_excel(file_path, sheet = "Severity-Mortality")
names(dta) <- c("age_category", "ifr", "ihr")
mort_sever_rv <- dta %>%
mutate(ihr = ihr/100) %>% # starting unit should be % - scaling to a value between 0 and 1
mutate(ifr = ifr/max(ifr)) # scaling to a value between 0 and 1
# Population
dta <- read_excel(file_path, sheet = "Population")
names(dta) <- c("age_category", "pop", "birth", "death")
population_rv <- dta %>%
transmute(age_category, pop, birth, death)
# Parameters
param <- bind_rows(read_excel(file_path, sheet = "Parameters"),
read_excel(file_path, sheet = "Country Area Param"),
read_excel(file_path, sheet = "Virus Param"),
read_excel(file_path, sheet = "Hospitalisation Param"),
read_excel(file_path, sheet = "Interventions Param"),
read_excel(file_path, sheet = "Interventions")) %>%
mutate(Value_Date = as.Date(Value_Date))
# START Bridge ----
popstruc <- population_rv %>%
select(age_category, pop) %>%
rename(agefloor = age_category) %>%
as.data.frame()
popbirth <- population_rv %>%
select(age_category, birth) %>%
as.data.frame() # unit should be per person per day
mort <- population_rv %>%
pull(death) # unit should be per person per day
ihr <- mort_sever_rv %>%
select(age_category, ihr) %>%
as.data.frame()
ifr <- mort_sever_rv %>%
select(age_category, ifr) %>%
as.data.frame()
######### POP AGEING
# per year ageing matrix
A<-length(popstruc[,2])
dd<-seq(1:A)/seq(1:A)
ageing <- t(diff(diag(dd),lag=1)/(5*365.25))
ageing<-cbind(ageing,0*seq(1:A)) # no ageing from last compartment
#
# pop<-population$country==country_name
# pp<-population$pop[pop]
### CONTACT MATRICES
c_home <- contact_home[[country_name]] %>% as.matrix()
c_school <- contact_school[[country_name]] %>% as.matrix()
c_work <- contact_work[[country_name]] %>% as.matrix()
c_other <- contact_other[[country_name]] %>% as.matrix()
nce <-A-length(c_home[1,])
contact_home<-matrix(0,nrow=A,ncol=A)
contact_school<-matrix(0,nrow=A,ncol=A)
contact_work<-matrix(0,nrow=A,ncol=A)
contact_other<-matrix(0,nrow=A,ncol=A)
for (i in 1:(A-nce)){
for (j in 1:(A-nce)){
contact_home[i,j]<-c_home[i,j]
contact_school[i,j]<-c_school[i,j]
contact_work[i,j]<-c_work[i,j]
contact_other[i,j]<-c_other[i,j]
}
}
for (i in (A+1-nce):A){
for (j in 1:(A-nce)){
contact_home[i,j]<-c_home[(A-nce),j]
contact_school[i,j]<-c_school[(A-nce),j]
contact_work[i,j]<-c_work[(A-nce),j]
contact_other[i,j]<-c_other[(A-nce),j]
}
}
for (i in 1:(A-nce)){
for (j in (A+1-nce):A){
contact_home[i,j]<-c_home[i,(A-nce)]
contact_school[i,j]<-c_school[i,(A-nce)]
contact_work[i,j]<-c_work[i,(A-nce)]
contact_other[i,j]<-c_other[i,(A-nce)]
}
}
for (i in (A+1-nce):A){
for (j in (A+1-nce):A){
contact_home[i,j]<-c_home[(A-nce),(A-nce)]
contact_school[i,j]<-c_school[(A-nce),(A-nce)]
contact_work[i,j]<-c_work[(A-nce),(A-nce)]
contact_other[i,j]<-c_other[(A-nce),(A-nce)]
}
}
######### INITIALISE SIMULATION/INTERVENTION START TIMES
startdate <- param$Value_Date[param$Parameter == "date_range_simul_start"]
stopdate <- param$Value_Date[param$Parameter == "date_range_simul_end"]
startdate <- startdate[1]
stopdate <- stopdate[1]
day_start <- as.numeric(startdate-startdate)
day_stop <- as.numeric(stopdate-startdate)
times <- seq(day_start, day_stop)
tin<-as.numeric(startdate-as.Date("2020-01-01"))/365.25
initP<-sum(popstruc[,2]) # population size
ageindcase<-20 # age of index case (years)
aci <- floor((ageindcase/5)+1) # age class of index case
############# DEFINE PARAMETERS
parameters <- c(
### Transmission instrinsic
p = param$Value[param$Parameter=="p"][1],
rho = param$Value[param$Parameter=="rho"][1],
omega = param$Value[param$Parameter=="omega"][1],
gamma = param$Value[param$Parameter=="gamma"][1],
nui = param$Value[param$Parameter=="nui"][1],
report = param$Value[param$Parameter=="report"][1],
reportc = param$Value[param$Parameter=="reportc"][1],
reporth = param$Value[param$Parameter=="reporth"][1],
beds_available = param$Value[param$Parameter=="beds_available"][1],
icu_beds_available = param$Value[param$Parameter=="icu_beds_available"][1],
ventilators_available = param$Value[param$Parameter=="ventilators_available"][1],
give = 95,
pdeath_h = mean( param$Value[param$Parameter=="pdeath_h"],na.rm=T),
pdeath_ho = mean( param$Value[param$Parameter=="pdeath_ho"],na.rm=T),
pdeath_hc = mean( param$Value[param$Parameter=="pdeath_hc"],na.rm=T),
pdeath_hco = mean( param$Value[param$Parameter=="pdeath_hco"],na.rm=T),
pdeath_icu = mean( param$Value[param$Parameter=="pdeath_icu"],na.rm=T),
pdeath_icuo = mean( param$Value[param$Parameter=="pdeath_icuo"],na.rm=T),
pdeath_icuc = mean( param$Value[param$Parameter=="pdeath_icuc"],na.rm=T),
pdeath_icuco = mean( param$Value[param$Parameter=="pdeath_icuco"],na.rm=T),
pdeath_vent = mean( param$Value[param$Parameter=="pdeath_vent"],na.rm=T),
pdeath_ventc = mean( param$Value[param$Parameter=="pdeath_ventc"],na.rm=T),
ihr_scaling = param$Value[param$Parameter=="ihr_scaling"][1],
nus = param$Value[param$Parameter=="nus"][1],
nusc = param$Value[param$Parameter=="nus"][1], # nusc = nus
nu_icu = param$Value[param$Parameter=="nu_icu"][1],
nu_icuc = param$Value[param$Parameter=="nu_icu"][1], # nu_icuc = nu_icu
nu_vent = param$Value[param$Parameter=="nu_vent"][1],
nu_ventc = param$Value[param$Parameter=="nu_vent"][1], # nu_ventc = nu_vent
rhos = param$Value[param$Parameter=="rhos"][1],
amp = param$Value[param$Parameter=="amp"][1],
phi = param$Value[param$Parameter=="phi"][1],
pclin = param$Value[param$Parameter=="pclin"][1],
prob_icu = param$Value[param$Parameter=="prob_icu"][1],
prob_vent = param$Value[param$Parameter=="prob_vent"][1],
propo2 = param$Value[param$Parameter=="propo2"][1],
dexo2 = mean( param$Value[param$Parameter=="dexo2"],na.rm=T),
dexo2c = mean( param$Value[param$Parameter=="dexo2c"],na.rm=T),
dexv = mean( param$Value[param$Parameter=="dexvc"],na.rm=T),
dexvc = mean( param$Value[param$Parameter=="dexvc"],na.rm=T),
vent_dex = mean(param$Value[param$Parameter=="vent_dex"],na.rm=T),
prob_icu_v = mean(param$Value[param$Parameter=="prob_icu_v"],na.rm=T),
prob_icu_vr = mean(param$Value[param$Parameter=="prob_icu_vr"],na.rm=T),
prob_icu_r = mean(param$Value[param$Parameter=="prob_icu_r"],na.rm=T),
prob_v_v = mean(param$Value[param$Parameter=="prob_v_v"],na.rm=T),
prob_v_vr = mean(param$Value[param$Parameter=="prob_v_vr"],na.rm=T),
prob_v_r = mean(param$Value[param$Parameter=="prob_v_r"],na.rm=T),
pclin_v = mean(param$Value[param$Parameter=="pclin_v"],na.rm=T),
pclin_vr = mean(param$Value[param$Parameter=="pclin_vr"],na.rm=T),
pclin_r = mean(param$Value[param$Parameter=="pclin_r"],na.rm=T),
sigmaEV = mean(param$Value[param$Parameter=="sigmaEV"],na.rm=T),
sigmaEVR = mean(param$Value[param$Parameter=="sigmaEVR"],na.rm=T),
sigmaER = mean(param$Value[param$Parameter=="sigmaER"],na.rm=T),
sigmaR = mean(param$Value[param$Parameter=="sigmaR"],na.rm=T),
vac_dur = mean(param$Value[param$Parameter=="vac_dur"],na.rm=T),
vac_dur_r = mean(param$Value[param$Parameter=="vac_dur_r"],na.rm=T),
report_natdeathI = mean(param$Value[param$Parameter=="report_natdeathI"],na.rm=T),
report_natdeathCL = mean(param$Value[param$Parameter=="report_natdeathCL"],na.rm=T),
pre = mean(param$Value[param$Parameter=="pre"],na.rm=T),
report_v = param$Value[param$Parameter=="report_v"][1],
report_cv = param$Value[param$Parameter=="report_cv"][1],
report_vr = param$Value[param$Parameter=="report_vr"][1],
report_cvr = param$Value[param$Parameter=="report_cvr"][1],
report_r = param$Value[param$Parameter=="report_r"][1],
report_cr = param$Value[param$Parameter=="report_cr"][1],
reporth_ICU = param$Value[param$Parameter=="reporth_ICU"][1],
report_death_HC = param$Value[param$Parameter=="report_death_HC"][1],
pdeath_vent_hc = mean( param$Value[param$Parameter=="pdeath_vent_hc"],na.rm=T),
pdeath_icu_hc = mean( param$Value[param$Parameter=="pdeath_icu_hc"],na.rm=T),
pdeath_icu_hco = mean( param$Value[param$Parameter=="pdeath_icu_hco"],na.rm=T),
reporth_g = param$Value[param$Parameter=="reporth_g"][1],
seroneg = param$Value[param$Parameter=="seroneg"][1],
sample_size = param$Value[param$Parameter=="sample_size"][1],
### INTERVENTIONS
# self isolation
selfis_eff = mean(param$Value[param$Parameter=="selfis_eff"],na.rm=T),
# social distancing
dist_eff = mean(param$Value[param$Parameter=="dist_eff"],na.rm=T),
# hand washing
hand_eff = mean(param$Value[param$Parameter=="hand_eff"],na.rm=T),
# mask wearing
mask_eff = mean(param$Value[param$Parameter=="mask_eff"],na.rm=T),
# working at home
work_eff = mean(param$Value[param$Parameter=="work_eff"],na.rm=T),
w2h = mean(param$Value[param$Parameter=="w2h"],na.rm=T),
# school closures
school_eff = mean(param$Value[param$Parameter=="school_eff"],na.rm=T),
s2h = mean(param$Value[param$Parameter=="s2h"],na.rm=T),
# cocooning the elderly
cocoon_eff = mean(param$Value[param$Parameter=="cocoon_eff"],na.rm=T),
age_cocoon = mean(param$Value[param$Parameter=="age_cocoon"],na.rm=T),
# vaccination campaign
# vaccine_on = as.numeric(param$Value_Date[param$Parameter=="date_vaccine_on"] - startdate),
vaccine_eff = mean(param$Value[param$Parameter=="vaccine_eff"],na.rm=T),
vaccine_eff_r = mean(param$Value[param$Parameter=="vaccine_eff_r"],na.rm=T),
age_vaccine_min = mean(param$Value[param$Parameter=="age_vaccine_min"],na.rm=T),
age_vaccine_max = mean(param$Value[param$Parameter=="age_vaccine_max"],na.rm=T),
# vaccine_cov = param$Value[param$Parameter=="vaccine_cov"],
vac_campaign = mean(param$Value[param$Parameter=="vac_campaign"],na.rm=T),
# travel ban
mean_imports = mean(param$Value[param$Parameter=="mean_imports"],na.rm=T),
# screening
screen_test_sens = mean(param$Value[param$Parameter=="screen_test_sens"],na.rm=T),
# screen_contacts = mean(param$Value[param$Parameter=="screen_contacts"],na.rm=T),
screen_overdispersion = mean(param$Value[param$Parameter=="screen_overdispersion"],na.rm=T),
# voluntary home quarantine
quarantine_days = mean(param$Value[param$Parameter=="quarantine_days"],na.rm=T),
quarantine_effort = mean(param$Value[param$Parameter=="quarantine_effort"],na.rm=T),
quarantine_eff_home = mean(param$Value[param$Parameter=="quarantine_eff_home"],na.rm=T),
quarantine_eff_other = mean(param$Value[param$Parameter=="quarantine_eff_other"],na.rm=T),
# mass testing
age_testing_min = mean(param$Value[param$Parameter=="age_testing_min"],na.rm=T),
age_testing_max = mean(param$Value[param$Parameter=="age_testing_max"],na.rm=T),
mass_test_sens = mean(param$Value[param$Parameter=="mass_test_sens"],na.rm=T),
isolation_days = mean(param$Value[param$Parameter=="isolation_days"],na.rm=T),
### Initialisation
init = param$Value[param$Parameter=="init"][1],
### Others
household_size = param$Value[param$Parameter=="household_size"][1],
noise = param$Value[param$Parameter=="noise"][1],
iterations = param$Value[param$Parameter=="iterations"][1],
confidence = param$Value[param$Parameter=="confidence"][1]
)
ihr[,2]<- parameters["ihr_scaling"]*ihr[,2]
parameters["ifr_correction_young"]<-1
parameters["ifr_correction_old"]<-1
# ifr[1:12,2]<-ifr[1:12,2]/ifr_correction_young
# ihr$ihr[15:21]<-ihr$ihr[15:21]*ifr_correction_old
# Scale parameters to percentages/ rates
parameters["rho"]<-parameters["rho"]/100
parameters["omega"]<-(1/(parameters["omega"]*365))
parameters["gamma"]<-1/parameters["gamma"]
parameters["nui"]<-1/parameters["nui"]
parameters["report"]<-parameters["report"]/100
parameters["reportc"]<-parameters["reportc"]/100
parameters["report_v"]<-parameters["report_v"]/100
parameters["report_cv"]<-parameters["report_cv"]/100
parameters["report_vr"]<-parameters["report_vr"]/100
parameters["report_cvr"]<-parameters["report_cvr"]/100
parameters["report_r"]<-parameters["report_r"]/100
parameters["report_cr"]<-parameters["report_cr"]/100
parameters["reporth"]<-parameters["reporth"]/100
parameters["nus"]<-1/parameters["nus"]
parameters["rhos"]<-parameters["rhos"]/100
parameters["amp"]<-parameters["amp"]/100
parameters["selfis_eff"]<-parameters["selfis_eff"]/100
parameters["dist_eff"]<-parameters["dist_eff"]/100
parameters["hand_eff"]<-parameters["hand_eff"]/100
parameters["mask_eff"]<-parameters["mask_eff"]/100
parameters["work_eff"]<-parameters["work_eff"]/100
parameters["w2h"]<-parameters["w2h"]/100
parameters["school_eff"]<-parameters["school_eff"]/100
parameters["s2h"]<-parameters["s2h"]/100
parameters["cocoon_eff"]<-parameters["cocoon_eff"]/100
parameters["age_cocoon"]<-floor((parameters["age_cocoon"]/5)+1)
parameters["vaccine_eff"]<-parameters["vaccine_eff"]/100
parameters["vaccine_eff_r"]<-parameters["vaccine_eff_r"]/100
age_vaccine_min<-(parameters["age_vaccine_min"])
age_vaccine_max<-(parameters["age_vaccine_max"])
# parameters["vaccine_cov"]<-parameters["vaccine_cov"]/100
# parameters["vac_campaign"]<-parameters["vac_campaign"]*7
parameters["screen_test_sens"]<-parameters["screen_test_sens"]/100
parameters["quarantine_days"]<-parameters["quarantine_days"]
parameters["quarantine_effort"]<-1/parameters["quarantine_effort"]
parameters["quarantine_eff_home"]<-parameters["quarantine_eff_home"]/-100
parameters["quarantine_eff_other"]<-parameters["quarantine_eff_other"]/100
parameters["give"]<-parameters["give"]/100
parameters["pdeath_h"]<-parameters["pdeath_h"]/100
parameters["pdeath_ho"]<-parameters["pdeath_ho"]/100
parameters["pdeath_hc"]<-parameters["pdeath_hc"]/100
parameters["pdeath_hco"]<-parameters["pdeath_hco"]/100
parameters["pdeath_icu"]<-parameters["pdeath_icu"]/100
parameters["pdeath_icuo"]<-parameters["pdeath_icuo"]/100
parameters["pdeath_icuc"]<-parameters["pdeath_icuc"]/100
parameters["pdeath_icuco"]<-parameters["pdeath_icuco"]/100
parameters["pdeath_vent"]<-parameters["pdeath_vent"]/100
parameters["pdeath_ventc"]<-parameters["pdeath_ventc"]/100
parameters["nusc"]<-1/parameters["nusc"]
parameters["nu_icu"]<-1/parameters["nu_icu"]
parameters["nu_icuc"]<-1/parameters["nu_icuc"]
parameters["nu_vent"]<-1/parameters["nu_vent"]
parameters["nu_ventc"]<-1/parameters["nu_ventc"]
parameters["pclin"]<-parameters["pclin"]/100
parameters["prob_icu"]<-parameters["prob_icu"]/100
parameters["prob_vent"]<-parameters["prob_vent"]/100
iterations<-parameters["iterations"]
noise<-parameters["noise"]
confidence<-parameters["confidence"]/100
parameters["mass_test_sens"]<-parameters["mass_test_sens"]/100
age_testing_min<-(parameters["age_testing_min"])
age_testing_max<-(parameters["age_testing_max"])
parameters["isolation_days"]<-parameters["isolation_days"]
parameters["propo2"]<-parameters["propo2"]/100
parameters["dexo2"]<-parameters["dexo2"]/100
parameters["dexo2c"]<-parameters["dexo2c"]/100
parameters["dexv"]<-parameters["dexv"]/100
parameters["dexvc"]<-parameters["dexvc"]/100
parameters["vent_dex"]<-parameters["vent_dex"]/100
parameters["prob_icu_v"]<-parameters["prob_icu_v"]/100
parameters["prob_icu_vr"]<-parameters["prob_icu_vr"]/100
parameters["prob_icu_r"]<-parameters["prob_icu_r"]/100
parameters["prob_v_v"]<-parameters["prob_v_v"]/100
parameters["prob_v_r"]<-parameters["prob_v_r"]/100
parameters["prob_v_vr"]<-parameters["prob_v_vr"]/100
parameters["pclin_v"]<-parameters["pclin_v"]/100
parameters["pclin_vr"]<-parameters["pclin_vr"]/100
parameters["pclin_r"]<-parameters["pclin_r"]/100
parameters["sigmaEV"]<-parameters["sigmaEV"]/100
parameters["sigmaER"]<-parameters["sigmaER"]/100
parameters["sigmaEVR"]<-parameters["sigmaEVR"]/100
parameters["sigmaR"]<-parameters["sigmaR"]/100
parameters["vac_dur"]<-1/parameters["vac_dur"]/100
parameters["vac_dur_r"]<-1/parameters["vac_dur_r"]/100
parameters["report_natdeathI"]<-parameters["report_natdeathI"]/100
parameters["report_natdeathCL"]<-parameters["report_natdeathCL"]/100
parameters["report_death_HC"]<-parameters["report_death_HC"]/100
parameters["reporth_ICU"]<-parameters["reporth_ICU"]/100
parameters["pre"]<-parameters["pre"]/100
parameters["pdeath_vent_hc"]<-parameters["pdeath_vent_hc"]/100
parameters["pdeath_icu_hc"]<-parameters["pdeath_icu_hc"]/100
parameters["pdeath_icu_hco"]<-parameters["pdeath_icu_hco"]/100
parameters["reporth_g"]<-parameters["reporth_g"]/100
parameters["seroneg"]<-(1/parameters["seroneg"])
parameters_noise <- c("p", "rho", "omega", "gamma", "nui", "ihr_scaling","nus", "nu_icu","nu_vent",
"rhos", "selfis_eff", "dist_eff", "hand_eff", "mask_eff", "work_eff",
"w2h", "s2h", "cocoon_eff", "mean_imports", "screen_overdispersion",
"quarantine_effort", "quarantine_eff_home", "quarantine_eff_other")
# parameters_fit <- c("p", "ihr_scaling","ifr_correction_young","ifr_correction_old","init")
parameters_fit <- rownames(fit_mat)
###########################################################################
# Define the indices for each variable
Sindex<-1:A
Eindex<-(A+1):(2*A)
Iindex<-(2*A+1):(3*A)
Rindex<-(3*A+1):(4*A)
Xindex<-(4*A+1):(5*A)
Hindex<-(5*A+1):(6*A)
HCindex<-(6*A+1):(7*A)
Cindex<-(7*A+1):(8*A)
CMindex<-(8*A+1):(9*A)
Vindex<-(9*A+1):(10*A)
QSindex<-(10*A+1):(11*A)
QEindex<-(11*A+1):(12*A)
QIindex<-(12*A+1):(13*A)
QRindex<-(13*A+1):(14*A)
CLindex<-(14*A+1):(15*A)
QCindex<-(15*A+1):(16*A)
ICUindex<-(16*A+1):(17*A)
ICUCindex<-(17*A+1):(18*A)
ICUCVindex<-(18*A+1):(19*A)
Ventindex<-(19*A+1):(20*A)
VentCindex<-(20*A+1):(21*A)
CMCindex<-(21*A+1):(22*A)
Zindex<-(22*A+1):(23*A)
EVindex<-(23*A+1):(24*A)
ERindex<-(24*A+1):(25*A)
EVRindex<-(25*A+1):(26*A)
VRindex<-(26*A+1):(27*A)
QVindex<-(27*A+1):(28*A)
QEVindex<-(28*A+1):(29*A)
QEVRindex<-(29*A+1):(30*A)
QERindex<-(30*A+1):(31*A)
QVRindex<-(31*A+1):(32*A)
HCICUindex<-(32*A+1):(33*A)
HCVindex<-(33*A+1):(34*A)
Abindex<-(34*A+1):(35*A)
###########################################################################
# MODEL INITIAL CONDITIONS
initI<-0*popstruc[,2] # Infected and symptomatic
initE<-0*popstruc[,2] # Incubating
# initE[aci]<-1 # place random index case in E compartment
initE[aci]<-parameters["init"] # place random index case in E compartment
initR<-parameters["pre"]*popstruc[,2] # Immune
initX<-0*popstruc[,2] # Isolated
initV<-0*popstruc[,2] # Vaccinated
initQS<-0*popstruc[,2] # quarantined S
initQE<-0*popstruc[,2] # quarantined E
initQI<-0*popstruc[,2] # quarantined I
initQR<-0*popstruc[,2] # quarantined R
initH<-0*popstruc[,2] # hospitalised
initHC<-0*popstruc[,2] # hospital critical
initC<-0*popstruc[,2] # Cumulative cases (true)
initCM<-0*popstruc[,2] # Cumulative deaths (true)
initCL<-0*popstruc[,2] # symptomatic cases
initQC<-0*popstruc[,2] # quarantined C
initICU<-0*popstruc[,2] # icu
initICUC<-0*popstruc[,2] # icu critical
initICUCV<-0*popstruc[,2] # icu critical
initVent<-0*popstruc[,2] # icu vent
initVentC<-0*popstruc[,2] # icu vent crit
initCMC<-0*popstruc[,2] # Cumulative deaths - overload (true)
initZ<-0*popstruc[,2] # testing - quarantined (true)
initEV<-0*popstruc[,2] # vaccinated exposed
initER<-0*popstruc[,2] # recovered exposed
initEVR<-0*popstruc[,2] # recovered and vaccinated exposed
initVR<-0*popstruc[,2] # recovered and vaccinated
initQV<-0*popstruc[,2] # quarantined and vaccinated
initQEV<-0*popstruc[,2] # quarantined, exposed and vaccinated
initQEVR<-0*popstruc[,2] # quarantined, exposed, recovered and vaccinated
initQER<-0*popstruc[,2] # quarantined, exposed and recovered
initQVR<-0*popstruc[,2] # quarantined, recovered and vaccinated
initHCICU<-0*popstruc[,2] # icu not seeking
initHCV<-0*popstruc[,2] # ventilator not seeking
initAb<-0*popstruc[,2] # ventilator not seeking
initS<-popstruc[,2]-initE-initI-initCL-initR-initX-initZ-initV-initH-initHC-initICU-initICUC-initICUCV-initVent-initVentC-
initQS-initQE-initQI-initQR-initQC-initEV-initER-initEVR-initVR-initQV-initQEV-initQEVR-initQER-initQVR-
initHCICU-initHCV # Susceptible (non-immune)
inp <- read_excel(file_path, sheet = "Interventions") %>%
filter(! is.na(Intervention))
# Test if listed interventions are valid
valid_interventions_v17 <- c("Dexamethasone", "Handwashing", "International Travel Ban",
"Mask Wearing", "Mass Testing", "School Closures", "Self-isolation if Symptomatic",
"(*Self-isolation) Household Isolation", "(*Self-isolation) Screening", "Shielding the Elderly",
"Social Distancing", "Vaccination", "Working at Home")
if(all(inp$Intervention %in% valid_interventions_v17)) message("Okay, all interventions are valid.")
if(!all(inp$Intervention %in% valid_interventions_v17)) stop("Stop, some interventions are not valid.")
# complte the age_groups column
inp$`Age Groups`[is.na(inp$`Age Groups`)] <- "1-21"
vec<- inp$`Age Groups`
parse_age_group <- function(vec) {
regx_str <- "^(([0]*([1-9]|1[0-9]|2[0-1]))[\\,\\:])*([0]*([1-9]|1[0-9]|2[0-1]))$"
output <- rep(FALSE, 21)
vec <- str_replace_all(vec, "-", ":")
vec <- str_replace_all(vec, ";", ",")
vec <- str_replace_all(vec, "[:space:]", "")
vec <- str_replace_all(vec, "[:alpha:]", "")
if (!str_detect(vec, regx_str)) {
return(output)
}
vec <- paste0("c(", vec, ")")
vec2 <- eval(parse(text = vec))
output[vec2] <- TRUE
return(output)
}
inp$Target<-rep(0,length(inp$Value))
age_group_vectors <- list()
for (i in 1:length(vec)){
pp<-parse_age_group(vec[i])
age_group_vectors[[i]] <- pp
inp$Target[i]<-i
}
inputs<-function(inp, run){
# cap intervention start and end dates with simulation end date
inp$`Date Start` = pmin(stopdate, inp$`Date Start`)
inp$`Date End` = pmin(stopdate, inp$`Date End`)
inp <- inp %>% arrange(`Date Start`)
# print(inp)
tv<-which(inp$`Apply to`==run)
si<-intersect(which(inp$Intervention=="Self-isolation if Symptomatic"),tv)
scr<-intersect(which(inp$Intervention=="Screening (when S.I.)"),tv)
sd<-intersect(which(inp$Intervention=="Social Distancing"),tv)
hw<-intersect(which(inp$Intervention=="Handwashing"),tv)
msk<-intersect(which(inp$Intervention=="Masking"),tv)
wah<-intersect(which(inp$Intervention=="Working at Home"),tv)
sc<-intersect(which(inp$Intervention=="School Closures"),tv)
# scc<-intersect(which(inp$Intervention=="School Group Code"),tv)
cte<-intersect(which(inp$Intervention=="Shielding the Elderly"),tv)
q<-intersect(which(inp$Intervention=="Household Isolation (when S.I.)"),tv)
tb<-intersect(which(inp$Intervention=="International Travel Ban"),tv)
vc<-intersect(which(inp$Intervention=="Vaccination"),tv)
mt<-intersect(which(inp$Intervention=="Mass Testing"),tv)
vc<-intersect(which(inp$Intervention=="Vaccination"),tv)
dx<-intersect(which(inp$Intervention=="Dexamethasone"),tv)
v<-(format(as.POSIXct(inp$`Date Start`,format='%Y/%m/%d %H:%M:%S'),format="%d/%m/%y"))
v2<-as.Date(v,format="%d/%m/%y")
inp$`Date Start`<-v2
v<-(format(as.POSIXct(inp$`Date End`,format='%Y/%m/%d %H:%M:%S'),format="%d/%m/%y"))
v2<-as.Date(v,format="%d/%m/%y")
inp$`Date End`<-v2
## self isolation
f<-c()
si_vector<-c()
isolation<-c()
if (length(si)>=1){
for (i in 1:length(si)){
f<-c(f,as.numeric(inp$`Date Start`[si[i]]-startdate),as.numeric(inp$`Date End`[si[i]]-startdate))
# print(f)
if(i==1){
if (inp$`Date Start`[si[i]]>startdate){
si_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[si[i]],(f[i+1]-f[i])*20))
isolation<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
si_vector<-c(rep(inp$`Value`[si[i]],(f[i+1])*20))
isolation<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
si_vector<-c(si_vector,rep(inp$`Value`[si[i]],20))
isolation<-c(isolation,rep(1,20))
}else{
si_vector<-c(si_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
isolation<-c(isolation,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
si_vector<-c(si_vector,rep(inp$`Value`[si[i]],(f[i*2]-f[i*2-1])*20))
isolation<-c(isolation,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(si) && f[i*2]<tail(times,1)){
si_vector<-c(si_vector,rep(0,(tail(times,1)-f[i*2])*20))
isolation<-c(isolation,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
si_vector<-rep(0,tail(times,1)*20)
isolation<-rep(0,tail(times,1)*20)
}
## social distancing
f<-c()
sd_vector<-c()
distancing<-c()
if (length(sd)>=1){
for (i in 1:length(sd)){
f<-c(f,as.numeric(inp$`Date Start`[sd[i]]-startdate),as.numeric(inp$`Date End`[sd[i]]-startdate))
if(i==1){
if (inp$`Date Start`[sd[i]]>startdate){
sd_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[sd[i]],(f[i+1]-f[i])*20))
distancing<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
sd_vector<-c(rep(inp$`Value`[sd[i]],(f[i+1])*20))
distancing<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
sd_vector<-c(sd_vector,rep(inp$`Value`[sd[i]],20))
distancing<-c(distancing,rep(1,20))
}else{
sd_vector<-c(sd_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
distancing<-c(distancing,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
sd_vector<-c(sd_vector,rep(inp$`Value`[sd[i]],(f[i*2]-f[i*2-1])*20))
distancing<-c(distancing,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(sd)&& f[i*2]<tail(times,1)){
sd_vector<-c(sd_vector,rep(0,(tail(times,1)-f[i*2])*20))
distancing<-c(distancing,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
sd_vector<-rep(0,tail(times,1)*20)
distancing<-rep(0,tail(times,1)*20)
}
## screening
f<-c()
scr_vector<-c()
screen<-c()
if (length(scr)>=1){
for (i in 1:length(scr)){
f<-c(f,as.numeric(inp$`Date Start`[scr[i]]-startdate),as.numeric(inp$`Date End`[scr[i]]-startdate))
if(i==1){
if (inp$`Date Start`[scr[i]]>startdate){
scr_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[scr[i]],(f[i+1]-f[i])*20))
screen<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
scr_vector<-c(rep(inp$`Value`[scr[i]],(f[i+1])*20))
screen<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
scr_vector<-c(scr_vector,rep(inp$`Value`[scr[i]],20))
screen<-c(screen,rep(1,20))
}else{
scr_vector<-c(scr_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
screen<-c(screen,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
scr_vector<-c(scr_vector,rep(inp$`Value`[scr[i]],(f[i*2]-f[i*2-1])*20))
screen<-c(screen,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(scr)&& f[i*2]<tail(times,1)){
scr_vector<-c(scr_vector,rep(0,(tail(times,1)-f[i*2])*20))
screen<-c(screen,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
scr_vector<-rep(0,tail(times,1)*20)
screen<-rep(0,tail(times,1)*20)
}
## handwashing
f<-c()
hw_vector<-c()
handwash<-c()
if (length(hw)>=1){
for (i in 1:length(hw)){
f<-c(f,as.numeric(inp$`Date Start`[hw[i]]-startdate),as.numeric(inp$`Date End`[hw[i]]-startdate))
if(i==1){
if (inp$`Date Start`[hw[i]]>startdate){
hw_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[hw[i]],(f[i+1]-f[i])*20))
handwash<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
hw_vector<-c(rep(inp$`Value`[hw[i]],(f[i+1])*20))
handwash<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
hw_vector<-c(hw_vector,rep(inp$`Value`[hw[i]],20))
handwash<-c(handwash,rep(1,20))
}else{
hw_vector<-c(hw_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
handwash<-c(handwash,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
hw_vector<-c(hw_vector,rep(inp$`Value`[hw[i]],(f[i*2]-f[i*2-1])*20))
handwash<-c(handwash,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(hw)&& f[i*2]<tail(times,1)){
hw_vector<-c(hw_vector,rep(0,(tail(times,1)-f[i*2])*20))
handwash<-c(handwash,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
hw_vector<-rep(0,tail(times,1)*20)
handwash<-rep(0,tail(times,1)*20)
}
## masking
f<-c()
msk_vector<-c()
masking<-c()
if (length(msk)>=1){
for (i in 1:length(msk)){
f<-c(f,as.numeric(inp$`Date Start`[msk[i]]-startdate),as.numeric(inp$`Date End`[msk[i]]-startdate))
if(i==1){
if (inp$`Date Start`[msk[i]]>startdate){
msk_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[msk[i]],(f[i+1]-f[i])*20))
masking<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
msk_vector<-c(rep(inp$`Value`[msk[i]],(f[i+1])*20))
masking<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
msk_vector<-c(msk_vector,rep(inp$`Value`[msk[i]],20))
masking<-c(masking,rep(1,20))
}else{
msk_vector<-c(msk_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
masking<-c(masking,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
msk_vector<-c(msk_vector,rep(inp$`Value`[msk[i]],(f[i*2]-f[i*2-1])*20))
masking<-c(masking,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(msk)&& f[i*2]<tail(times,1)){
msk_vector<-c(msk_vector,rep(0,(tail(times,1)-f[i*2])*20))
masking<-c(masking,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
msk_vector<-rep(0,tail(times,1)*20)
masking<-rep(0,tail(times,1)*20)
}
## dexamethasone
f<-c()
dex<-c()
if (length(dx)>=1){
for (i in 1:length(dx)){
f<-c(f,as.numeric(inp$`Date Start`[dx[i]]-startdate),as.numeric(inp$`Date End`[dx[i]]-startdate))
if(i==1){
if (inp$`Date Start`[dx[i]]>startdate){
dex<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
dex<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
dex<-c(dex,rep(1,20))
}else{
dex<-c(dex,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
dex<-c(dex,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(dx)&& f[i*2]<tail(times,1)){
dex<-c(dex,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
dex<-rep(0,tail(times,1)*20)
}
## working at home
f<-c()
wah_vector<-c()
workhome<-c()
if (length(wah)>=1){
for (i in 1:length(wah)){
f<-c(f,as.numeric(inp$`Date Start`[wah[i]]-startdate),as.numeric(inp$`Date End`[wah[i]]-startdate))
if(i==1){
if (inp$`Date Start`[wah[i]]>startdate){
wah_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[wah[i]],(f[i+1]-f[i])*20))
workhome<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
wah_vector<-c(rep(inp$`Value`[wah[i]],(f[i+1])*20))
workhome<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
wah_vector<-c(wah_vector,rep(inp$`Value`[wah[i]],20))
workhome<-c(workhome,rep(1,20))
}else{
wah_vector<-c(wah_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
workhome<-c(workhome,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
wah_vector<-c(wah_vector,rep(inp$`Value`[wah[i]],(f[i*2]-f[i*2-1])*20))
workhome<-c(workhome,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(wah)&& f[i*2]<tail(times,1)){
wah_vector<-c(wah_vector,rep(0,(tail(times,1)-f[i*2])*20))
workhome<-c(workhome,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
wah_vector<-rep(0,tail(times,1)*20)
workhome<-rep(0,tail(times,1)*20)
}
## school closure
f<-c()
sc_vector<-c()
schoolclose<-c()
if (length(sc)>=1){
for (i in 1:length(sc)){
f<-c(f,as.numeric(inp$`Date Start`[sc[i]]-startdate),as.numeric(inp$`Date End`[sc[i]]-startdate))
if(i==1){
if (inp$`Date Start`[sc[i]]>startdate){
sc_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[sc[i]],(f[i+1]-f[i])*20))
schoolclose<-c(rep(0,f[i]*20),rep(inp$Target[sc[i]],(f[i+1]-f[i])*20))
}
else{
sc_vector<-c(rep(inp$`Value`[sc[i]],(f[i+1])*20))
schoolclose<-c(rep(inp$Target[sc[i]],(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
sc_vector<-c(sc_vector,rep(inp$`Value`[sc[i]],20))
schoolclose<-c(schoolclose,rep(inp$Target[sc[i]],20))
}else{
sc_vector<-c(sc_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
schoolclose<-c(schoolclose,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
sc_vector<-c(sc_vector,rep(inp$`Value`[sc[i]],(f[i*2]-f[i*2-1])*20))
schoolclose<-c(schoolclose,rep(inp$Target[sc[i]],(f[i*2]-f[i*2-1])*20))
}
if(i==length(sc)&& f[i*2]<tail(times,1)){
sc_vector<-c(sc_vector,rep(0,(tail(times,1)-f[i*2])*20))
schoolclose<-c(schoolclose,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
sc_vector<-rep(0,tail(times,1)*20)
schoolclose<-rep(0,tail(times,1)*20)
}
schoolclose[is.na(schoolclose)]<-1
## cocooning the elderly
f<-c()
cte_vector<-c()
cocoon<-c()
if (length(cte)>=1){
for (i in 1:length(cte)){
f<-c(f,as.numeric(inp$`Date Start`[cte[i]]-startdate),as.numeric(inp$`Date End`[cte[i]]-startdate))
if(i==1){
if (inp$`Date Start`[cte[i]]>startdate){
cte_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[cte[i]],(f[i+1]-f[i])*20))
cocoon<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
cte_vector<-c(rep(inp$`Value`[cte[i]],(f[i+1])*20))
cocoon<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
cte_vector<-c(cte_vector,rep(inp$`Value`[cte[i]],20))
cocoon<-c(cocoon,rep(1,20))
}else{
cte_vector<-c(cte_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
cocoon<-c(cocoon,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
cte_vector<-c(cte_vector,rep(inp$`Value`[cte[i]],(f[i*2]-f[i*2-1])*20))
cocoon<-c(cocoon,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(cte)&& f[i*2]<tail(times,1)){
cte_vector<-c(cte_vector,rep(0,(tail(times,1)-f[i*2])*20))
cocoon<-c(cocoon,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
cte_vector<-rep(0,tail(times,1)*20)
cocoon<-rep(0,tail(times,1)*20)
}
## quarantine
f<-c()
q_vector<-c()
quarantine<-c()
if (length(q)>=1){
for (i in 1:length(q)){
f<-c(f,as.numeric(inp$`Date Start`[q[i]]-startdate),as.numeric(inp$`Date End`[q[i]]-startdate))
if(i==1){
if (inp$`Date Start`[q[i]]>startdate){
q_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[q[i]],(f[i+1]-f[i])*20))
quarantine<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
q_vector<-c(rep(inp$`Value`[q[i]],(f[i+1])*20))
quarantine<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
q_vector<-c(q_vector,rep(inp$`Value`[q[i]],20))
quarantine<-c(quarantine,rep(1,20))
}else{
q_vector<-c(q_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
quarantine<-c(quarantine,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
q_vector<-c(q_vector,rep(inp$`Value`[q[i]],(f[i*2]-f[i*2-1])*20))
quarantine<-c(quarantine,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(q)&& f[i*2]<tail(times,1)){
q_vector<-c(q_vector,rep(0,(tail(times,1)-f[i*2])*20))
quarantine<-c(quarantine,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
q_vector<-rep(0,tail(times,1)*20)
quarantine<-rep(0,tail(times,1)*20)
}
## travel ban
f<-c()
tb_vector<-c()
travelban<-c()
if (length(tb)>=1){
for (i in 1:length(tb)){
f<-c(f,as.numeric(inp$`Date Start`[tb[i]]-startdate),as.numeric(inp$`Date End`[tb[i]]-startdate))
if(i==1){
if (inp$`Date Start`[tb[i]]>startdate){
tb_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[tb[i]],(f[i+1]-f[i])*20))
travelban<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
tb_vector<-c(rep(inp$`Value`[tb[i]],(f[i+1])*20))
travelban<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
tb_vector<-c(tb_vector,rep(inp$`Value`[tb[i]],20))
travelban<-c(travelban,rep(1,20))
}else{
tb_vector<-c(tb_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
travelban<-c(travelban,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
tb_vector<-c(tb_vector,rep(inp$`Value`[tb[i]],(f[i*2]-f[i*2-1])*20))
travelban<-c(travelban,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(tb)&& f[i*2]<tail(times,1)){
tb_vector<-c(tb_vector,rep(0,(tail(times,1)-f[i*2])*20))
travelban<-c(travelban,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
tb_vector<-rep(0,tail(times,1)*20)
travelban<-rep(0,tail(times,1)*20)
}
## mass testing
f<-c()
mt_vector<-c()
masstesting<-c()
testage<-c()
if (length(mt)>=1){
for (i in 1:length(mt)){
f<-c(f,as.numeric(inp$`Date Start`[mt[i]]-startdate),as.numeric(inp$`Date End`[mt[i]]-startdate))
if(i==1){
if (inp$`Date Start`[mt[i]]>startdate){
mt_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[mt[i]],(f[i+1]-f[i])*20))
masstesting<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
testage<-c(rep(0,f[i]*20),rep(inp$`Target`[mt[i]],(f[i+1]-f[i])*20))
}
else{
mt_vector<-c(rep(inp$`Value`[mt[i]],(f[i+1])*20))
masstesting<-c(rep(1,(f[i+1])*20))
testage<-c(rep(inp$`Target`[mt[i]],(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
mt_vector<-c(mt_vector,rep(inp$`Value`[mt[i]],20))
masstesting<-c(masstesting,rep(1,20))
testage<-c(testage,rep(inp$`Target`[mt[i]],20))
}else{
mt_vector<-c(mt_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
masstesting<-c(masstesting,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
testage<-c(testage,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
mt_vector<-c(mt_vector,rep(inp$`Value`[mt[i]],(f[i*2]-f[i*2-1])*20))
masstesting<-c(masstesting,rep(1,(f[i*2]-f[i*2-1])*20))
testage<-c(testage,rep(inp$`Target`[mt[i]],(f[i*2]-f[i*2-1])*20))
}
if(i==length(mt)&& f[i*2]<tail(times,1)){
mt_vector<-c(mt_vector,rep(0,(tail(times,1)-f[i*2])*20))
masstesting<-c(masstesting,rep(0,(tail(times,1)-f[i*2])*20))
testage<-c(testage,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
mt_vector<-rep(0,tail(times,1)*20)
masstesting<-rep(0,tail(times,1)*20)
testage<-rep(0,tail(times,1)*20)
}
## vaccine
f<-c()
vc_vector<-c()
vaccine<-c()
vaccineage<-c()
if (length(vc)>=1){
for (i in 1:length(vc)){
f<-c(f,as.numeric(inp$`Date Start`[vc[i]]-startdate),as.numeric(inp$`Date End`[vc[i]]-startdate))
if(i==1){
if (inp$`Date Start`[vc[i]]>startdate){
vc_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[vc[i]],(f[i+1]-f[i])*20))
vaccine<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
vaccineage<-c(rep(0,f[i]*20),rep(inp$`Target`[vc[i]],(f[i+1]-f[i])*20))
}
else{
vc_vector<-c(rep(inp$`Value`[vc[i]],(f[i+1])*20))
vaccine<-c(rep(1,(f[i+1])*20))
vaccineage<-c(rep(inp$`Target`[vc[i]],(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
vc_vector<-c(vc_vector,rep(inp$`Value`[vc[i]],20))
vaccine<-c(vaccine,rep(1,20))
vaccineage<-c(vaccineage,rep(inp$`Target`[vc[i]],20))
}else{
vc_vector<-c(vc_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
vaccine<-c(vaccine,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
vaccineage<-c(vaccineage,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
vc_vector<-c(vc_vector,rep(inp$`Value`[vc[i]],(f[i*2]-f[i*2-1])*20))
vaccine<-c(vaccine,rep(1,(f[i*2]-f[i*2-1])*20))
vaccineage<-c(vaccineage,rep(inp$`Target`[vc[i]],(f[i*2]-f[i*2-1])*20))
}
if(i==length(vc)&& f[i*2]<tail(times,1)){
vc_vector<-c(vc_vector,rep(0,(tail(times,1)-f[i*2])*20))
vaccine<-c(vaccine,rep(0,(tail(times,1)-f[i*2])*20))
vaccineage<-c(vaccineage,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
vc_vector<-rep(0,tail(times,1)*20)
vaccine<-rep(0,tail(times,1)*20)
vaccineage<-rep(0,tail(times,1)*20)
}
return(list(si_vector=si_vector,sd_vector=sd_vector,scr_vector=scr_vector,hw_vector=hw_vector,msk_vector=msk_vector,
wah_vector=wah_vector,sc_vector=sc_vector,tb_vector=tb_vector,mt_vector=mt_vector*1000,
cte_vector=cte_vector,q_vector=q_vector,vc_vector=vc_vector,isolation=isolation,
screen=screen,cocoon=cocoon,schoolclose=schoolclose,workhome=workhome,handwash=handwash,masking=masking,
quarantine=quarantine,vaccine=vaccine,travelban=travelban,distancing=distancing,masstesting=masstesting,
testage=testage,vaccineage=vaccineage,dex=dex))
}
vectors0<-inputs(inp,'Baseline (Calibration)')
vectors<-inputs(inp,'Hypothetical Scenario')
f <- c(1,(1+parameters["give"])/2,(1-parameters["give"])/2,0)
KH<-parameters["beds_available"]
KICU<- parameters["icu_beds_available"]+parameters["ventilators_available"]
Kvent<- parameters["ventilators_available"]
x.H <- c(0,(1+parameters["give"])*KH/2,(3-parameters["give"])*KH/2,2*KH)
x.ICU <- c(0,(1+parameters["give"])*KICU/2,(3-parameters["give"])*KICU/2,2*KICU)
x.Vent <- c(0,(1+parameters["give"])*Kvent/2,(3-parameters["give"])*Kvent/2,2*Kvent)
fH <- splinefun(x.H, f, method = "hyman")
fICU <- splinefun(x.ICU, f, method = "hyman")
fVent<- splinefun(x.Vent, f, method = "hyman")
# set up a function to solve the equations
covid<-function(t, Y, parameters,input)
{
with(as.list(c(Y, parameters)),
{
S <- Y[Sindex]
E <- Y[Eindex]
I <- Y[Iindex]
R <- Y[Rindex]
X <- Y[Xindex]
Z <- Y[Zindex]
H <- Y[Hindex]
HC <- Y[HCindex]
C <- Y[Cindex]
CM <- Y[CMindex]
V <- Y[Vindex]
QS <- Y[QSindex]
QE <- Y[QEindex]
QI <- Y[QIindex]
QR <- Y[QRindex]
CL <- Y[CLindex]
QC <- Y[QCindex]
ICU <- Y[ICUindex]
ICUC <- Y[ICUCindex]
ICUCV <-Y[ICUCVindex]
Vent <- Y[Ventindex]
VentC <- Y[VentCindex]
CMC <- Y[CMCindex]
EV <- Y[EVindex]
ER <- Y[ERindex]
EVR <- Y[EVRindex]
VR <- Y[VRindex]
QV <- Y[QVindex]
QEV <- Y[QEVindex]
QER <- Y[QERindex]
QEVR <- Y[QEVRindex]
QVR <- Y[QVRindex]
HCICU <- Y[HCICUindex]
HCV <- Y[HCVindex]
Ab <- Y[Abindex]
P <- (S+E+I+R+X+Z+V+H+HC+ICU+ICUC+ICUCV+Vent+VentC+EV+ER+EVR+VR+HCICU+HCV+
QS+QE+QI+QR+CL+QC+QEV+QV+QER+QEVR+QVR)
Q <- (sum(QS)+sum(QE)+sum(QI)+sum(QC)+sum(QR)+sum(QV)+sum(QER)+sum(QEVR)+sum(QEV)+sum(QVR))/sum(P)
# print(HCICUindex)
# print(sum(ER))
# print(t)
# print(paste("EVR",sum(QE)))
# print(paste("ER",sum(QR)))
# print(paste("QER",sum(QER)))
# print(paste("QEVR",sum(QEVR)))
# print(paste("QV",sum(QV)))
# print(paste("QC",sum(QC)))
# print(paste("QI",sum(QI)))
# print(paste("QEV",sum(QV)))
# print(paste("QE",sum(QC)))
# print(paste("QR",sum(QR)))
# health system performance
critH<-min(1-fH(sum(H)+sum(ICUC)+sum(ICUCV)),1)
crit<-min(1-fICU(sum(ICU)+sum(Vent)+sum(VentC)),1)
critV<-min(1-fVent(sum(Vent)),1)
# interventions
isolation<-input$isolation[t*20+1]
distancing<-input$distancing[t*20+1]
handwash<-input$handwash[t*20+1]
masking<-input$masking[t*20+1]
workhome<-input$workhome[t*20+1]
schoolclose<-input$schoolclose[t*20+1]
cocoon<-input$cocoon[t*20+1]
vaccine<-input$vaccine[t*20+1]
travelban<-input$travelban[t*20+1]
screen<-input$screen[t*20+1]
quarantine<-input$quarantine[t*20+1]
masstesting<-input$masstesting[t*20+1]
dexamethasone<-input$dex[t*20+1]
screen_eff<-0
selfis<-0
school<-1
dist<-1
hand<-0
mask<-0
vaccinate<-0
trvban_eff<-0
quarantine_rate<-0
tests_per_day<-0
selfis_cov<-(input$si_vector[t*20+1])/100
screen_contacts<-(input$scr_vector[t*20+1])/10
school_eff<-(input$sc_vector[t*20+1])/100
dist_cov<-(input$sd_vector[t*20+1])/100
hand_cov<-(input$hw_vector[t*20+1])/100
mask_cov<-(input$msk_vector[t*20+1])/100
cocoon<-(input$cte_vector[t*20+1])/100
work_cov<-(input$wah_vector[t*20+1])/100
travelban_eff<-(input$tb_vector[t*20+1])/100
vaccine_cov<-(input$vc_vector[t*20+1])/100
quarantine_cov<-(input$q_vector[t*20+1])/100
tests_per_day<-(input$mt_vector[t*20+1])
vaccineage<-input$vaccineage[t*20+1]
testage<-input$testage[t*20+1]
if (vaccine){
age_vaccine_vector<-as.numeric(age_group_vectors[[vaccineage]])
vac_rate<-(-log(1-vaccine_cov)/vac_campaign)
vaccinate<-vac_rate
}else{age_vaccine_vector<-rep(0,A)}
# print(vaccinate*age_vaccine_vector)
if (masstesting){
age_testing_vector<-as.numeric(age_group_vectors[[testage]])
}else{age_testing_vector<-rep(0,A)}
if (workhome){
work<-work_cov*work_eff
}else{work<-1}
if (isolation){
selfis<-selfis_cov
if(screen){
screen_eff<-min(sum(report*I+reportc*(CL)+H+ICU+Vent+reporth*(HC+ICUC+ICUCV+VentC+HCICU+HCV))*screen_contacts*(screen_overdispersion*I/P)*screen_test_sens/P,1)
}
}
if (schoolclose>=1){
school<-school_eff
schoolclose2<-as.numeric(age_group_vectors[[schoolclose]])
}else{schoolclose2<-0}
# print(schoolclose2)
# print(schoolclose)
if(distancing){
dist<-dist_cov*dist_eff
}
if(handwash){
hand<-hand_eff*hand_cov
}
if(masking){
mask<-mask_eff*mask_cov
}
if(travelban){
trvban_eff<-travelban_eff
}
if(quarantine){
rate_q<-min((min(sum((CL+H+ICU+Vent+HC+ICUC+ICUCV+VentC+HCV+HCICU))*(household_size-1)/sum(P),1)*quarantine_effort),quarantine_cov/2)
quarantine_rate<-rate_q/(1+exp(-10*(quarantine_cov/2-Q)))
}
if(dexamethasone){
prob_v<-prob_vent*vent_dex
}else{
dexo2<-1;dexo2c<-1;dexv<-1;dexvc<-1;prob_v<-prob_vent;
}
# print(paste(dexo2,dexo2c,dexv,dexvc,prob_v))
# print(paste("quarantine_rate",quarantine_rate))
# print(quarantine_rate*sum(R))
# print(quarantine_rate*ER)
#
# print(sum(vaccinate*age_vaccine_vector*R))
# print((1/quarantine_days)*sum(QR))
# testing rates
propI<-sum(I)/sum(P)
propC<-sum(CL)/sum(P)
propE<-sum(E)/sum(P)
propEV<-sum(EV)/sum(P)
propER<-sum(ER)/sum(P)
propEVR<-sum(EVR)/sum(P)
propHC<-sum(HC)/sum(P)
propHCICU<-sum(HCICU)/sum(P)
propHCV<-sum(HCV)/sum(P)
testE<-tests_per_day*propE
testEV<-tests_per_day*propEV
testER<-tests_per_day*propER
testEVR<-tests_per_day*propEVR
testI<-tests_per_day*propI
testC<-tests_per_day*propC
testHC<-tests_per_day*propHC
testHCICU<-tests_per_day*propHCICU
testHCV<-tests_per_day*propHCV
if(sum(I)>1){
ratetestI<-mass_test_sens*testI/sum(I)
# print(paste('rateI: ',ratetestI))
}else{ratetestI<-0}
if(sum(CL)>1){
ratetestC<-mass_test_sens*testC/sum(CL)
# print(paste('rateC: ',ratetestC))
}else{ratetestC<-0}
# print(sum(E))
if(sum(E)>1){
ratetestE<-mass_test_sens*testE/sum(E)
}else{ratetestE<-0}
if(sum(EV)>1){
ratetestEV<-mass_test_sens*testEV/sum(EV)
# print(paste('rateEV: ',ratetestEV))
}else{ratetestEV<-0}
if(sum(ER)>1){
ratetestER<-mass_test_sens*testER/sum(ER)
# print(paste('rateER: ',ratetestER))
}else{ratetestER<-0}
if(sum(EVR)>1){
ratetestEVR<-mass_test_sens*testEVR/sum(EVR)
}else{ratetestEVR<-0}
if(sum(HC)>1){
ratetestHC<-mass_test_sens*testHC/sum(HC)
}else{ratetestHC<-0}
if(sum(HCICU)>1){
ratetestHCICU<-mass_test_sens*testHCICU/sum(HCICU)
}else{ratetestHCICU<-0}
if(sum(HCV)>1){
ratetestHCV<-mass_test_sens*testHCV/sum(HCV)
}else{ratetestHCV<-0}
# print(mass_test_sens)
# print(ratetestI*sum(I) + ratetestC*sum(CL) - (1/isolation_days)*sum(Z) )
# print(propC)
# print(testI)
# print(testC)
#
# cocooning the elderly
cocoon_mat<-matrix((1-cocoon_eff),nrow = length(popstruc$pop),ncol = length(popstruc$pop))
cocoon_mat[1:(age_cocoon-1),1:(age_cocoon-1)]<-1
# contact matrices
cts<-(contact_home+distancing*(1-dist)*contact_other+(1-distancing)*contact_other
+(1-schoolclose2)*contact_school # school on
+schoolclose2*(1-school)*contact_school # school close
+schoolclose2*contact_home*school*s2h # inflating contacts at home when school closes
+(1-workhome)*contact_work # normal work
+workhome*(1-work)*contact_work # people not working from home when homework is active
+contact_home*workhome*work*w2h # inflating contacts at home when working from home
)
# Final transmission related parameters
contacts <- (1-cocoon)*cts+cocoon*cts*cocoon_mat+cocoon*(1+schoolclose2*(1-school_eff)+workhome*(1-work_eff))*contact_home*(1-cocoon_mat)
seas <- 1+amp*cos(2*3.14*(t-(phi*365.25/12))/365.25)
importation <- mean_imports*(1-trvban_eff)
HH<-H+ICU+Vent+ICUC+ICUCV+VentC
HHC<-HC+HCICU+HCV
lam <- (1-max(hand,mask))*p*seas*(contacts%*%((rho*E+(I+CL+importation)+(1-selfis_eff)*(X+HHC)+rhos*(HH))/P))+
(1-max(hand,mask))*p*seas*(1-quarantine*quarantine_eff_other)*(contact_other%*%((rho*QE+QI+QC+QEV+QEVR+QER)/P))
# contacts under home quarantine
lamq<-(1-max(hand,mask))*p*seas*((1-quarantine_eff_home)*contact_home%*%(((1-selfis_eff)*(X+HHC+rho*QE+QI+QC++QEV+QEVR+QER))/P))+
(1-max(hand,mask))*p*seas*(1-quarantine_eff_other)*(contact_other%*%((rho*E+(I+CL+importation)+(1-selfis_eff)*(X+HHC+rho*QE+QI+QC++QEV+QEVR+QER)+rhos*(HH))/P))
# lamq<-0
# print(paste("lamq",lamq))
# print(paste("lamq",(1-vaccine_eff_r)*lamq*sum(QVR) ))
# print(paste("quarantine evr",quarantine_rate*sum(EVR) ))
# birth/death
b1<-sum(popbirth[,2]*popstruc[,2])
birth<-0*popbirth[,2]
birth[1]<-b1
# ODE system
dSdt <- -S*lam-vaccinate*age_vaccine_vector*S+omega*R+vac_dur*V-
quarantine_rate*S +(1/quarantine_days)*QS+ageing%*%S-mort*S+birth
dEdt <- S*lam-gamma*E+ageing%*%E- vaccinate*age_vaccine_vector*E - mort*E -
quarantine_rate*E+(1/quarantine_days)*QE
dIdt <- gamma*(1-pclin)*(1-age_testing_vector*ratetestE)*(1-screen_eff)*(1-ihr[,2])*E+
gamma*(1-pclin_v)*(1-age_testing_vector*ratetestEV)*(1-screen_eff)*(1-sigmaEV*ihr[,2])*EV+
gamma*(1-pclin_vr)*(1-age_testing_vector*ratetestEVR)*(1-screen_eff)*(1-sigmaEVR*ihr[,2])*EVR+
gamma*(1-pclin_r)*(1-age_testing_vector*ratetestER)*(1-screen_eff)*(1-sigmaER*ihr[,2])*ER-
vaccinate*age_vaccine_vector*I - nui*I+ageing%*%I-mort*I +
(1/quarantine_days)*QI - quarantine_rate*I - ratetestI*age_testing_vector*I
dCLdt<- gamma*pclin*(1-age_testing_vector*ratetestE)*(1-selfis)*(1-ihr[,2])*(1-quarantine_rate)*E+
gamma*pclin_v*(1-age_testing_vector*ratetestEV)*(1-selfis)*(1-sigmaEV*ihr[,2])*(1-quarantine_rate)*EV+
gamma*pclin_vr*(1-age_testing_vector*ratetestEVR)*(1-selfis)*(1-sigmaEVR*ihr[,2])*(1-quarantine_rate)*EVR+
gamma*pclin_r*(1-age_testing_vector*ratetestER)*(1-selfis)*(1-sigmaER*ihr[,2])*(1-quarantine_rate)*ER-
nui*CL+ageing%*%CL-mort*CL + (1/quarantine_days)*QC - ratetestC*age_testing_vector*CL
dRdt <- vac_dur_r*VR-omega*R-vaccinate*age_vaccine_vector*R-lam*sigmaR*R - quarantine_rate*R+
nui*I+nui*X+nui*CL+ageing%*%R-mort*R + (1/isolation_days)*Z+(1/quarantine_days)*QR+
nus*propo2*(1-dexo2*pdeath_ho)*ifr[,2]*H+nus*(1-propo2)*(1-pdeath_h)*ifr[,2]*H+
nusc*propo2*(1-pdeath_hco)*ifr[,2]*HC+nusc*(1-propo2)*(1-pdeath_hc)*ifr[,2]*HC+
nusc*propo2*(1-pdeath_icu_hco)*ifr[,2]*HCICU+nusc*(1-propo2)*(1-pdeath_icu_hc)*ifr[,2]*HCICU+
nu_icu*propo2*(1-dexo2*pdeath_icuo)*ifr[,2]*ICU+nu_icu*(1-propo2)*(1-pdeath_icu)*ifr[,2]*ICU+
nu_icuc*propo2*(1-dexo2c*pdeath_icuco)*ifr[,2]*ICUC+nu_icuc*(1-propo2)*(1-pdeath_icuc)*ifr[,2]*ICUC+
nu_vent*(1-dexv*pdeath_vent)*ifr[,2]*Vent+
nu_ventc*(1-pdeath_vent_hc)*ifr[,2]*HCV+
nu_ventc*(1-dexvc*pdeath_ventc)*ifr[,2]*VentC+nu_ventc*(1-dexvc*pdeath_ventc)*ifr[,2]*ICUCV
dXdt <- gamma*selfis*(1-age_testing_vector*ratetestE)*pclin*(1-ihr[,2])*E+
gamma*(1-pclin)*(1-age_testing_vector*ratetestE)*screen_eff*(1-ihr[,2])*E+
gamma*selfis*(1-age_testing_vector*ratetestEV)*pclin_v*(1-sigmaEV*ihr[,2])*EV+
gamma*(1-pclin_v)*(1-age_testing_vector*ratetestEV)*screen_eff*(1-sigmaEV*ihr[,2])*EV+
gamma*selfis*(1-age_testing_vector*ratetestEVR)*pclin_v*(1-sigmaEVR*ihr[,2])*EVR+
gamma*(1-pclin_vr)*(1-age_testing_vector*ratetestEVR)*screen_eff*(1-sigmaEVR*ihr[,2])*EVR+
gamma*selfis*(1-age_testing_vector*ratetestER)*pclin_r*(1-sigmaER*ihr[,2])*ER+
gamma*(1-pclin_r)*(1-age_testing_vector*ratetestER)*screen_eff*(1-sigmaER*ihr[,2])*ER+
-nui*X+ageing%*%X-mort*X
dVdt <- vaccinate*age_vaccine_vector*S + omega*VR - (1-vaccine_eff)*lam*V - vac_dur*V + ageing%*%V-mort*V - quarantine_rate*V
dEVdt<- (1-vaccine_eff)*lam*V - gamma*EV + ageing%*%EV - mort*EV - quarantine_rate*EV +(1/quarantine_days)*QEV
dERdt<- lam*sigmaR*R - gamma*ER + ageing%*%ER - mort*ER - quarantine_rate*ER +(1/quarantine_days)*QER
dVRdt <- vaccinate*age_vaccine_vector*E + vaccinate*age_vaccine_vector*I + vaccinate*age_vaccine_vector*R -
(1-vaccine_eff_r)*lam*VR - vac_dur_r*VR + ageing%*%VR - mort*VR - omega*VR - quarantine_rate*VR + (1/quarantine_days)*QVR
dEVRdt<- (1-vaccine_eff_r)*lam*VR - gamma*EVR + ageing%*%EVR-mort*EVR - quarantine_rate*EVR +
(1/quarantine_days)*QEVR
dQSdt <- quarantine_rate*S + ageing%*%QS-mort*QS - (1/quarantine_days)*QS - lamq*QS
dQEdt <- quarantine_rate*E - gamma*QE + ageing%*%QE-mort*QE - (1/quarantine_days)*QE + lamq*QS
dQIdt <- quarantine_rate*I + gamma*(1-ihr[,2])*(1-pclin)*QE+
gamma*(1-sigmaEV*ihr[,2])*(1-pclin_v)*QEV+
gamma*(1-sigmaER*ihr[,2])*(1-pclin_r)*QER+
gamma*(1-sigmaEVR*ihr[,2])*(1-pclin_vr)*QEVR-
nui*QI+ageing%*%QI-mort*QI - (1/quarantine_days)*QI
dQCdt <- gamma*pclin*(1-selfis)*(1-age_testing_vector*ratetestE)*(1-ihr[,2])*quarantine_rate*E+
gamma*pclin_v*(1-age_testing_vector*ratetestEV)*(1-selfis)*(1-sigmaEV*ihr[,2])*quarantine_rate*EV+
gamma*pclin_vr*(1-age_testing_vector*ratetestEVR)*(1-selfis)*(1-sigmaEVR*ihr[,2])*quarantine_rate*EVR+
gamma*pclin_r*(1-age_testing_vector*ratetestER)*(1-selfis)*(1-sigmaER*ihr[,2])*quarantine_rate*ER+
gamma*(1-ihr[,2])*pclin*QE +
gamma*(1-sigmaEV*ihr[,2])*pclin_v*QEV +
gamma*(1-sigmaER*ihr[,2])*pclin_r*QER +
gamma*(1-sigmaEVR*ihr[,2])*pclin_vr*QEVR -
nui*QC+ageing%*%QC-mort*QC - (1/quarantine_days)*QC
dQRdt <- quarantine_rate*R + nui*QI + nui*QC + ageing%*%QR-mort*QR - (1/quarantine_days)*QR + vac_dur_r*QVR
dQVdt <- quarantine_rate*V + ageing%*%QV-mort*QV - (1/quarantine_days)*QV - (1-vaccine_eff)*lamq*QV + omega*QVR
dQEVdt <- quarantine_rate*EV - gamma*QEV + ageing%*%QEV-mort*QEV - (1/quarantine_days)*QEV + (1-vaccine_eff)*lamq*QV
dQERdt <- quarantine_rate*ER - gamma*QER + ageing%*%QER-mort*QER - (1/quarantine_days)*QER + sigmaR*lamq*QR
dQVRdt <- quarantine_rate*VR - (1-vaccine_eff_r)*lam*QVR - vac_dur_r*QVR - omega*QVR + ageing%*%QVR - mort*QVR
dQEVRdt <- quarantine_rate*EVR - gamma*QEVR +ageing%*%QEVR-mort*QEVR -
(1/quarantine_days)*QEVR +(1-vaccine_eff_r)*lamq*QVR
dHdt <- gamma*ihr[,2]*(1-prob_icu)*(1-critH)*reporth*E+
gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*(1-critH)*reporth*EV +
gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*(1-critH)*reporth*EVR +
gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*(1-critH)*reporth*ER +
gamma*ihr[,2]*(1-prob_icu)*(1-critH)*reporth*QE +
gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*(1-critH)*reporth*QEV +
gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*(1-critH)*reporth*QEVR +
gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*(1-critH)*reporth*QER -
nus*H + ageing%*%H-mort*H
dHCdt <- gamma*ihr[,2]*(1-prob_icu)*(1-reporth)*E+gamma*ihr[,2]*(1-prob_icu)*critH*reporth*E +
gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*(1-reporth)*EV+gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*critH*reporth*EV+
gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*(1-reporth)*EVR+gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*critH*reporth*EVR+
gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*(1-reporth)*ER+gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*critH*reporth*ER +
gamma*ihr[,2]*(1-prob_icu)*(1-reporth)*QE+gamma*ihr[,2]*(1-prob_icu)*critH*reporth*QE+
gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*(1-reporth)*QEV+gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*critH*reporth*QEV+
gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*(1-reporth)*QEVR+gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*critH*reporth*QEVR+
gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*(1-reporth)*QER+gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*critH*reporth*QER -
nusc*HC + ageing%*%HC-mort*HC - ratetestHC*age_testing_vector*HC
dHCICUdt <- gamma*(1-reporth_ICU)*ihr[,2]*prob_icu*(1-prob_v)*E+
gamma*(1-reporth_ICU)*sigmaEV*ihr[,2]*prob_icu_v*(1-prob_v_v)*EV+
gamma*(1-reporth_ICU)*sigmaEVR*ihr[,2]*prob_icu_vr*(1-prob_v_vr)*EVR+
gamma*(1-reporth_ICU)*sigmaER*ihr[,2]*prob_icu_r*(1-prob_v_r)*ER+
gamma*(1-reporth_ICU)*ihr[,2]*prob_icu*(1-prob_v)*QE+
gamma*(1-reporth_ICU)*sigmaEV*ihr[,2]*prob_icu_v*(1-prob_v_v)*QEV+
gamma*(1-reporth_ICU)*sigmaEVR*ihr[,2]*prob_icu_vr*(1-prob_v_vr)*QEVR+
gamma*(1-reporth_ICU)*sigmaER*ihr[,2]*prob_icu_r*(1-prob_v_r)*QER-
nusc*HCICU + ageing%*%HCICU-mort*HCICU - ratetestHCICU*age_testing_vector*HCICU
dHCVdt <- gamma*(1-reporth_ICU)*ihr[,2]*prob_icu*prob_v*E+
gamma*(1-reporth_ICU)*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*EV+
gamma*(1-reporth_ICU)*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*EVR+
gamma*(1-reporth_ICU)*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*ER+
gamma*(1-reporth_ICU)*ihr[,2]*prob_icu*prob_v*QE+
gamma*(1-reporth_ICU)*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*QEV+
gamma*(1-reporth_ICU)*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*QEVR+
gamma*(1-reporth_ICU)*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*QER-
nu_ventc*HCV + ageing%*%HCV-mort*HCV - ratetestHCV*age_testing_vector*HCV
dICUdt <- gamma*reporth_ICU*ihr[,2]*prob_icu*(1-crit)*(1-prob_v)*E+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*(1-crit)*(1-prob_v_v)*EV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*(1-crit)*(1-prob_v_vr)*EVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*(1-crit)*(1-prob_v_r)*ER+
gamma*reporth_ICU*ihr[,2]*prob_icu*(1-crit)*(1-prob_v)*QE+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*(1-crit)*(1-prob_v_v)*QEV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*(1-crit)*(1-prob_v_vr)*QEVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*(1-crit)*(1-prob_v_r)*QER -
nu_icu*ICU +ageing%*%ICU - mort*ICU + (1-crit)*ICUC*1/2
dICUCdt <- gamma*reporth_ICU*ihr[,2]*prob_icu*crit*(1-prob_v)*E+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*crit*(1-prob_v_v)*EV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*crit*(1-prob_v_vr)*EVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*crit*(1-prob_v_r)*ER+
gamma*reporth_ICU*ihr[,2]*prob_icu*crit*(1-prob_v)*QE+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*crit*(1-prob_v_v)*QEV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*crit*(1-prob_v_vr)*QEVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*crit*(1-prob_v_r)*QER -
nu_icuc*ICUC -(1-crit)*ICUC*1/2 +ageing%*%ICUC - mort*ICUC
dICUCVdt <- gamma*reporth_ICU*ihr[,2]*prob_icu*prob_v*crit*E+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*crit*EV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*crit*EVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*crit*ER+
gamma*reporth_ICU*ihr[,2]*prob_icu*prob_v*crit*QE+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*crit*QEV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*crit*QEVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*crit*QER -
nu_ventc*ICUCV +ageing%*%ICUCV - mort*ICUCV - (1-critV)*ICUCV*1/2
dVentdt <- gamma*reporth_ICU*ihr[,2]*prob_icu*(1-crit)*(1-critV)*prob_v*E+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*(1-crit)*(1-critV)*prob_v_v*EV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*(1-crit)*(1-critV)*prob_v_vr*EVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*(1-crit)*(1-critV)*prob_v_r*ER+
gamma*reporth_ICU*ihr[,2]*prob_icu*(1-crit)*(1-critV)*prob_v*QE+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*(1-crit)*(1-critV)*prob_v_v*QEV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*(1-crit)*(1-critV)*prob_v_vr*QEVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*(1-crit)*(1-critV)*prob_v_r*QER +
(1-critV)*VentC*1/2 +(1-critV)*ICUCV*1/2 - nu_vent*Vent +ageing%*%Vent - mort*Vent
dVentCdt <- gamma*reporth_ICU*ihr[,2]*prob_icu*prob_v*(1-crit)*critV*E+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*(1-crit)*critV*EV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*(1-crit)*critV*EVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*(1-crit)*critV*ER+
gamma*reporth_ICU*ihr[,2]*prob_icu*prob_v*(1-crit)*critV*QE+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*(1-crit)*critV*QEV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*(1-crit)*critV*QEVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*(1-crit)*critV*QER -
(1-critV)*VentC*1/2 -nu_ventc*VentC +ageing%*%VentC - mort*VentC
# print(dexo2)
dCdt <- report*gamma*(1-age_testing_vector*ratetestE)*(1-pclin)*(1-ihr[,2])*(E+QE)+reportc*gamma*pclin*(1-age_testing_vector*ratetestE)*(1-ihr[,2])*(E+QE)+
gamma*ihr[,2]*(1-critH)*(1-prob_icu)*(E+QE)+gamma*ihr[,2]*critH*reporth*(1-prob_icu)*(E+QE)+
gamma*ihr[,2]*prob_icu*(E+QE)+ratetestI*age_testing_vector*I+ratetestC*age_testing_vector*CL+gamma*age_testing_vector*ratetestE*(1-ihr[,2])*E
dCMdt<- nus*propo2*dexo2*pdeath_ho*ifr[,2]*H+nus*(1-propo2)*pdeath_h*ifr[,2]*H+
nusc*report_death_HC*propo2*pdeath_hco*ifr[,2]*HC+nusc*report_death_HC*(1-propo2)*pdeath_hc*ifr[,2]*HC+
nu_icu*propo2*dexo2*pdeath_icuo*ifr[,2]*ICU+nu_icu*(1-propo2)*pdeath_icu*ifr[,2]*ICU+
nu_icuc*propo2*dexo2c*pdeath_icuco*ifr[,2]*ICUC+nu_icuc*(1-propo2)*pdeath_icuc*ifr[,2]*ICUC+
nu_vent*dexv*pdeath_vent*ifr[,2]*Vent+nu_ventc*dexvc*pdeath_ventc*ifr[,2]*VentC +
nu_ventc*dexvc*pdeath_ventc*ifr[,2]*ICUCV+ nu_ventc*report_death_HC*pdeath_vent_hc*ifr[,2]*HCV+
nu_icuc*report_death_HC*propo2*pdeath_icu_hco*ifr[,2]*HCICU+nu_icuc*report_death_HC*(1-propo2)*pdeath_icu_hc*ifr[,2]*HCICU +
mort*H + mort*ICU + mort*ICUC + mort*ICUCV + mort*Vent + mort*VentC + mort*Z +
mort*report_death_HC*HC +mort*report_death_HC*HCICU + mort*report_death_HC*HCV +
report_natdeathI*mort*I + report_natdeathI*mort*QI+ report_natdeathI*mort*E+
report_natdeathI*mort*QE + report_natdeathI*mort*EV+ report_natdeathI*mort*EVR+
report_natdeathI*mort*ER + report_natdeathI*mort*QEV+
report_natdeathI*mort*QEVR + report_natdeathI*mort*QER+
report_natdeathCL*mort*CL + report_natdeathCL*mort*QC + report_natdeathCL*mort*X
dCMCdt <- nusc*propo2*pdeath_hco*ifr[,2]*HC+nusc*(1-propo2)*pdeath_hc*ifr[,2]*HC+
nu_icuc*propo2*dexo2c*pdeath_icuco*ifr[,2]*ICUC+nu_icuc*(1-propo2)*pdeath_icuc*ifr[,2]*ICUC+
nu_ventc*dexvc*pdeath_ventc*ifr[,2]*VentC+nu_ventc*dexvc*pdeath_ventc*ifr[,2]*ICUCV+
mort*HC + mort*ICUC + mort*VentC + mort*ICUCV
dZdt <- gamma*ratetestE*age_testing_vector*(1-ihr[,2])*E+
ratetestI*age_testing_vector*I+
ratetestC*age_testing_vector*CL+
gamma*(1-ihr[,2])*ratetestEV*age_testing_vector*EV+
gamma*(1-ihr[,2])*ratetestEVR*age_testing_vector*EVR+
gamma*(1-ihr[,2])*ratetestER*age_testing_vector*ER+
ratetestHC*age_testing_vector*HC+
ratetestHCICU*age_testing_vector*HCICU+
ratetestHCV*age_testing_vector*HCV-
(1/isolation_days)*Z-mort*Z
dAbdt <- nui*I+nui*X+nui*CL+
nus*propo2*(1-dexo2*pdeath_ho)*ifr[,2]*H+nus*(1-propo2)*(1-pdeath_h)*ifr[,2]*H+
nusc*propo2*(1-pdeath_hco)*ifr[,2]*HC+nusc*(1-propo2)*(1-pdeath_hc)*ifr[,2]*HC+
nusc*propo2*(1-pdeath_icu_hco)*ifr[,2]*HCICU+nusc*(1-propo2)*(1-pdeath_icu_hc)*ifr[,2]*HCICU+
nu_ventc*(1-pdeath_vent_hc)*ifr[,2]*HCV+
nu_icu*propo2*(1-dexo2*pdeath_icuo)*ifr[,2]*ICU+nu_icu*(1-propo2)*(1-pdeath_icu)*ifr[,2]*ICU+
nu_icuc*propo2*(1-dexo2c*pdeath_icuco)*ifr[,2]*ICUC+nu_icuc*(1-propo2)*(1-pdeath_icuc)*ifr[,2]*ICUC+
nu_vent*(1-dexv*pdeath_vent)*ifr[,2]*Vent+
nu_ventc*(1-dexvc*pdeath_ventc)*ifr[,2]*VentC+
nu_ventc*(1-dexvc*pdeath_ventc)*ifr[,2]*ICUCV -
seroneg*Ab - mort*Ab + ageing%*%Ab
# print(paste("QEVR",sum(QEVR)))
# return the rate of change
list(c(S=dSdt,dEdt,dIdt,dRdt,dXdt,dHdt,dHCdt,dCdt,dCMdt,dVdt,dQSdt,dQEdt,dQIdt,dQRdt,dCLdt,dQCdt,dICUdt,dICUCdt,dICUCVdt,
dVentdt,dVentCdt,dCMCdt,dZdt,dEVdt,dERdt,dEVRdt,dVRdt,dQVdt,dQEVdt,dQEVRdt,dQERdt,dQVRdt,dHCICUdt,dHCVdt,dAbdt))
}
)
}
########### RUN BASELINE MODEL - start time for interventions is set to day 1e5, i.e. interventions are always off
Y<-c(initS,initE,initI,initR,initX,initH,initHC,initC,initCM,initV, initQS, initQE, initQI, initQR, initCL, initQC, initICU,
initICUC, initICUCV, initVent, initVentC, initCMC,initZ, initEV, initER, initEVR, initVR,
initQV,initQEV,initQEVR,initQER,initQVR,initHCICU,initHCV,initAb) # initial conditions for the main solution vector
out0 <- ode(y = Y, times = times, method = "euler", hini = 0.05, func = covid, parms = parameters, input=vectors0)
tail(rowSums(out0[,(CMindex+1)]),1) # cumulative mortality
tail(rowSums(out0[,(Sindex+1)]),1)/sum(popstruc[,2]) # cumulative mortality
tail(rowSums(out0[,(Vindex+1)]),1)/sum(popstruc[,2]) # cumulative mortality
tail(rowSums(out0[,(Rindex+1)]),1)/sum(popstruc[,2]) # cumulative mortality
sum(rowSums(out0[,(Iindex+1)])) # cumulative mortality
sum(rowSums(out0[,(Ventindex+1)])) # cumulative mortality
sum(rowSums(out0[,(HCVindex+1)])) # cumulative mortality
sum(rowSums(out0[,(HCICUindex+1)])) # cumulative mortality
sum(rowSums(out0[,(HCindex+1)])) # cumulative mortality
# ############################# PLOT TIME VARYING INTERVENTIONS #####################################
# library(plyr)
# interv_timeseries<-ldply(vectors[c("wah_vector","tb_vector","si_vector","sc_vector","hw_vector","msk_vector")], data.frame)
# interv_timeseries<-cbind(interv_timeseries,rep(1:(length(vectors$si_vector)),6))
# colnames(interv_timeseries)<-c("Intervention","Coverage","Time")
#
# m4 <- interv_timeseries %>%
# # convert state to factor and reverse order of levels
# mutate(Intervention=factor(Intervention,levels=rev(sort(unique(Intervention))))) %>%
# # create a new variable from count
# mutate(covfactor=cut(Coverage,breaks=seq(0,100,by=5),
# labels=c("0-5","5-10","10-15","15-20","20-25","25-30",
# "30-35","35-40","40-45","45-50","50-55","55-60","60-65",
# "65-70","70-75","75-80","80-85","85-90","90-95","95-100"))) %>%
# # change level order
# mutate(covfactor=factor(as.character(covfactor),levels=rev(levels(covfactor))))
# levels(m4$Intervention)<-c("Work from home","Interntional Travel Ban","Self isolation","Screening","Mask wearing","Handwashing")
#
# cols<-c("black",brewer.pal(11,"Spectral"))
# textcol <- "grey40"
# p <- ggplot(m4,aes(x=Time,y=Intervention,fill=covfactor))+
# geom_tile( height = 0.9)+
# guides(fill=guide_legend(title="Coverage (%)"))+
# labs(x="",y="",title="")+
# scale_y_discrete(expand=c(0,0))+
# scale_fill_brewer(palette="Spectral")+
# # coord_fixed()+
# theme_grey(base_size=10)+
# theme(legend.position="right",legend.direction="vertical",
# legend.title=element_text(colour=textcol),
# legend.margin=margin(grid::unit(0,"cm")),
# legend.text=element_text(colour=textcol,size=7,face="bold"),
# legend.key.height=grid::unit(0.8,"cm"),
# legend.key.width=grid::unit(0.2,"cm"),
# axis.text.x=element_text(size=10,colour=textcol),
# axis.text.y=element_text(vjust=0.2,colour=textcol),
# axis.ticks=element_line(size=0.4),
# plot.background=element_blank(),
# panel.border=element_blank(),
# plot.margin=margin(0.7,0.4,0.1,0.2,"cm"),
# plot.title=element_text(colour=textcol,hjust=0,size=14,face="bold"))+
# theme_minimal()
# p
# ################################################################################################
process_ode_outcome <- function(out, iterations,intv_vector){
out_min<-out$min
out_max<-out$max
out_mean<-out$mean
critH<-c()
crit<-c()
critV<-c()
for (i in 1:length(times)){
critH[i]<-min(1-fH((sum(out_mean[i,(Hindex+1)]))+sum(out_mean[i,(ICUCindex+1)])+sum(out_mean[i,(ICUCVindex+1)])),1)
crit[i]<-min(1-fICU((sum(out_mean[i,(ICUindex+1)]))+(sum(out_mean[i,(Ventindex+1)]))+(sum(out_mean[i,(VentCindex+1)]))))
critV[i]<-min(1-fVent((sum(out_mean[i,(Ventindex+1)]))),1)
}
# total population
pop1<-out_mean[,(Sindex+1)]+out_mean[,(Eindex+1)]+out_mean[,(Iindex+1)]+out_mean[,(CLindex+1)]+out_mean[,(Rindex+1)]+
out_mean[,(Xindex+1)]+out_mean[,(Vindex+1)]+out_mean[,(Zindex+1)]+out_mean[,(EVindex+1)]+out_mean[,(ERindex+1)]+out_mean[,(EVRindex+1)]+
out_mean[,(QSindex+1)]+out_mean[,(QEindex+1)]+out_mean[,(QIindex+1)]+out_mean[,(QCindex+1)]+out_mean[,(QRindex+1)]+
out_mean[,(QVindex+1)]+out_mean[,(QEVindex+1)]+out_mean[,(QERindex+1)]+out_mean[,(QVRindex+1)]+out_mean[,(QEVRindex+1)]+
out_mean[,(Hindex+1)]+out_mean[,(HCindex+1)]+out_mean[,(ICUindex+1)]+out_mean[,(ICUCindex+1)]+out_mean[,(ICUCVindex+1)]+
out_mean[,(Ventindex+1)]+out_mean[,(VentCindex+1)]+out_mean[,(HCICUindex+1)]+out_mean[,(HCVindex+1)]
tpop1<-rowSums(pop1)
########################## AB prevalence
ab_age<-out_mean[,(Abindex+1)]
ab_all_ages<-rowSums(out_mean[,(Abindex+1)])
########################## CALCULATE MORTALITY
dexo2_hist <- rep(0,length(times))
dexo2c_hist <- rep(0,length(times))
dexv_hist <- rep(0,length(times))
dexvc_hist <- rep(0,length(times))
for (tt in times) {
if(tt < max(times)){
if(intv_vector$dex[tt*20+1]) {
dexo2_hist[tt+1] <- parameters["dexo2"]
dexo2c_hist[tt+1] <- parameters["dexo2c"]
dexv_hist[tt+1] <- parameters["dexv"]
dexvc_hist[tt+1] <- parameters["dexvc"]
} else {
dexo2_hist[tt+1] <- 1
dexo2c_hist[tt+1] <- 1
dexv_hist[tt+1] <- 1
dexvc_hist[tt+1] <- 1
}
} else {
dexo2_hist[tt+1] <- dexo2_hist[tt]
dexo2c_hist[tt+1] <- dexo2c_hist[tt]
dexv_hist[tt+1] <- dexv_hist[tt]
dexvc_hist[tt+1] <- dexvc_hist[tt]
}
}
cinc_mort_1 <- cumsum(rowSums(parameters["nus"]*parameters["propo2"]*parameters["pdeath_ho"]*dexo2_hist*(out_mean[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_2 <- cumsum(rowSums(parameters["nus"]*(1-parameters["propo2"])*parameters["pdeath_h"]*(out_mean[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_3 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_hco"]*(out_mean[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_4 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_hc"]*(out_mean[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_5 <- cumsum(rowSums(parameters["nu_icu"]*parameters["propo2"]*parameters["pdeath_icuo"]*dexo2_hist*(out_mean[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_6 <- cumsum(rowSums(parameters["nu_icu"]*(1-parameters["propo2"])*parameters["pdeath_icu"]*(out_mean[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_7 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icuco"]*dexo2c_hist*(out_mean[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_8 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icuc"]*(out_mean[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_9 <- cumsum(rowSums(parameters["nu_vent"]*parameters["pdeath_vent"]*dexv_hist*(out_mean[,(Ventindex+1)]%*%ifr[,2])))
cinc_mort_10 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_mean[,(VentCindex+1)]%*%ifr[,2])))
cinc_mort_11 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_mean[,(ICUCVindex+1)]%*%ifr[,2])))
cinc_mort_12 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_icu_hco"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_13 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_icu_hc"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_14 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["report_death_HC"]*parameters["pdeath_vent_hc"]*(out_mean[,(HCVindex+1)]%*%ifr[,2])))
cinc_mort_121 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icu_hco"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_131 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icu_hc"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_141 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_vent_hc"]*(out_mean[,(HCVindex+1)]%*%ifr[,2])))
cinc_mort_H1 <- cinc_mort_1 + cinc_mort_2
cinc_mort_HC1 <- cinc_mort_3 + cinc_mort_4 + cinc_mort_12 + cinc_mort_13 + cinc_mort_14
cinc_mort_ICU1 <- cinc_mort_5 + cinc_mort_6
cinc_mort_ICUC1 <- cinc_mort_7 + cinc_mort_8
cinc_mort_Vent1 <- cinc_mort_9
cinc_mort_VentC1 <- cinc_mort_10
cinc_mort_ICUCV1 <- cinc_mort_11
# all deaths due to covid19 disease - reported + unreported
cinc_mort_all<-cinc_mort_1+cinc_mort_2+cinc_mort_3+cinc_mort_4+cinc_mort_5+cinc_mort_6+
cinc_mort_7+cinc_mort_8+cinc_mort_9+cinc_mort_10+cinc_mort_11+cinc_mort_121+cinc_mort_131+cinc_mort_141
base_mort_H1 <- cumsum(rowSums(out_mean[,(Hindex+1)]%*%mort))
base_mort_HC1 <- cumsum(rowSums(parameters["report_death_HC"]*out_mean[,(HCindex+1)]%*%mort))
base_mort_ICU1 <- cumsum(rowSums(out_mean[,(ICUindex+1)]%*%mort))
base_mort_ICUC1 <- cumsum(rowSums(out_mean[,(ICUCindex+1)]%*%mort))
base_mort_ICUCV1 <- cumsum(rowSums(out_mean[,(ICUCVindex+1)]%*%mort))
base_mort_Vent1 <- cumsum(rowSums(out_mean[,(Ventindex+1)]%*%mort))
base_mort_VentC1 <- cumsum(rowSums(out_mean[,(VentCindex+1)]%*%mort))
base_mort_Z1 <- cumsum(rowSums(out_mean[,(Zindex+1)]%*%mort))
base_mort_HCICU1 <- cumsum(rowSums(parameters["report_death_HC"]*out_mean[,(HCICUindex+1)]%*%mort))
base_mort_HCV1 <- cumsum(rowSums(parameters["report_death_HC"]*out_mean[,(HCVindex+1)]%*%mort))
base_mort_V1 <- cumsum(rowSums(out_mean[,(Vindex+1)]%*%mort))
base_mort_S1 <- cumsum(rowSums(out_mean[,(Sindex+1)]%*%mort))
base_mort_QS1 <- cumsum(rowSums(out_mean[,(QSindex+1)]%*%mort))
base_mort_QR1 <- cumsum(rowSums(out_mean[,(QRindex+1)]%*%mort))
base_mort_R1 <- cumsum(rowSums(out_mean[,(Rindex+1)]%*%mort))
base_mort_QVR1 <- cumsum(rowSums(out_mean[,(QVRindex+1)]%*%mort))
base_mort_VR1 <- cumsum(rowSums(out_mean[,(VRindex+1)]%*%mort))
base_mort_QV1 <- cumsum(rowSums(out_mean[,(QVindex+1)]%*%mort))
base_mort_E1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(Eindex+1)]%*%mort))
base_mort_I1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(Iindex+1)]%*%mort))
base_mort_CL1 <- cumsum(rowSums(parameters["report_natdeathCL"]*out_mean[,(CLindex+1)]%*%mort))
base_mort_X1 <- cumsum(rowSums(parameters["report_natdeathCL"]*out_mean[,(Xindex+1)]%*%mort))
base_mort_QE1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(QEindex+1)]%*%mort))
base_mort_QI1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(QIindex+1)]%*%mort))
base_mort_QC1 <- cumsum(rowSums(parameters["report_natdeathCL"]*out_mean[,(QCindex+1)]%*%mort))
base_mort_ER1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(ERindex+1)]%*%mort))
base_mort_EV1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(EVindex+1)]%*%mort))
base_mort_EVR1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(EVRindex+1)]%*%mort))
base_mort_QEV1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(QEVindex+1)]%*%mort))
base_mort_QER1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(QERindex+1)]%*%mort))
base_mort_QEVR1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(QEVRindex+1)]%*%mort))
base_mort_HC11 <- cumsum(rowSums(out_mean[,(HCindex+1)]%*%mort))
base_mort_HCICU11 <- cumsum(rowSums(out_mean[,(HCICUindex+1)]%*%mort))
base_mort_HCV11 <- cumsum(rowSums(out_mean[,(HCVindex+1)]%*%mort))
base_mort_E11 <- cumsum(rowSums(out_mean[,(Eindex+1)]%*%mort))
base_mort_I11 <- cumsum(rowSums(out_mean[,(Iindex+1)]%*%mort))
base_mort_CL11 <- cumsum(rowSums(out_mean[,(CLindex+1)]%*%mort))
base_mort_X11 <- cumsum(rowSums(out_mean[,(Xindex+1)]%*%mort))
base_mort_QE11 <- cumsum(rowSums(out_mean[,(QEindex+1)]%*%mort))
base_mort_QI11 <- cumsum(rowSums(out_mean[,(QIindex+1)]%*%mort))
base_mort_QC11 <- cumsum(rowSums(out_mean[,(QCindex+1)]%*%mort))
base_mort_ER11 <- cumsum(rowSums(out_mean[,(ERindex+1)]%*%mort))
base_mort_EV11 <- cumsum(rowSums(out_mean[,(EVindex+1)]%*%mort))
base_mort_EVR11 <- cumsum(rowSums(out_mean[,(EVRindex+1)]%*%mort))
base_mort_QEV11 <- cumsum(rowSums(out_mean[,(QEVindex+1)]%*%mort))
base_mort_QER11 <- cumsum(rowSums(out_mean[,(QERindex+1)]%*%mort))
base_mort_QEVR11 <- cumsum(rowSums(out_mean[,(QEVRindex+1)]%*%mort))
# all deaths of infected with sars-cov-2 virus - reported + unreported
nat_deaths_inf <- round(base_mort_E11 + base_mort_I11 + base_mort_CL11 + base_mort_X11 +
base_mort_ER11 + base_mort_EV11+ base_mort_EVR11+
base_mort_QE11 + base_mort_QI11 + base_mort_QC11 +
base_mort_QEV11 + base_mort_QER11 + base_mort_QEVR11 + base_mort_Z1+
base_mort_H1+base_mort_HC11+base_mort_ICU1+base_mort_ICUC1+base_mort_ICUCV1+
base_mort_Vent1+base_mort_VentC1+base_mort_HCICU11+base_mort_HCV11)
# Export in a cohesive format ----
results <- list()
results$time <- startdate + times # dates
results$N <- tpop1
## Ab
results$ab_all_ages<-ab_all_ages
results$ab<-ab_age
# Rt/ FOI
dailyinc1<-out$mean_cases # daily incidence
results$Rt <- out$mean_Rt
results$pct_total_pop_infected <- out$mean_infections
results$doubling_time <- round(log(2)*7 / (log(dailyinc1[2+7] / dailyinc1[2])), 2) # (Baseline only) to double the number of infections at inception
results$daily_incidence <- round(dailyinc1) # daily incidence (Reported)
results$daily_total_cases <- round(out$mean_daily_infection) # daily incidence (Reported + Unreported) # daily incidence (Reported + Unreported)
# Hospital requirements
previcureq1<-rowSums(out_mean[,(Hindex+1)])+ rowSums(out_mean[,(ICUCindex+1)])+rowSums(out_mean[,(ICUCVindex+1)]) # surge beds occupancy
previcureq21<-rowSums(out_mean[,(ICUindex+1)])+rowSums(out_mean[,(VentCindex+1)]) # icu beds occupancy
previcureq31<-rowSums(out_mean[,(Ventindex+1)]) # ventilator occupancy
overloadH1<-rowSums(out_mean[,(HCindex+1)]) # requirement for beds
overloadICU1<-rowSums(out_mean[,(ICUCindex+1)])+rowSums(out_mean[,(HCICUindex+1)]) # requirement for icu beds
overloadICUV1<-rowSums(out_mean[,(ICUCVindex+1)]) # requirement for ventilators
overloadVent1<-rowSums(out_mean[,(VentCindex+1)])+rowSums(out_mean[,(HCVindex+1)]) # requirement for ventilators
results$required_beds <- round(previcureq1) # required beds
results$saturation <- parameters["beds_available"] # saturation
results$hospital_surge_beds <- round(previcureq1)
results$icu_beds <- round(previcureq21)
results$ventilators <- round(previcureq31)
results$normal_bed_requirement <- round(rowSums(out_mean[,(Hindex+1)])+overloadH1) #real required beds. previcureq1 above is the occupancy
results$icu_bed_requirement <- round(rowSums(out_mean[,(ICUindex+1)])+overloadICU1)
results$icu_ventilator_requirement <- round(rowSums(out_mean[,(Ventindex+1)])+overloadICUV1+overloadVent1)
### MORTALITY
results$cum_mortality <- round(rowSums(out_mean[,(CMindex+1)])) # cumulative mortality
results$deaths_from_covid<-last(cinc_mort_all)
results$deaths_with_covid<-last(nat_deaths_inf)
results$death_natural_non_exposed <- round(base_mort_S1+base_mort_V1+base_mort_QS1)
results$death_natural_exposed <- round(base_mort_E1 + base_mort_I1 + base_mort_CL1 + base_mort_X1 +
base_mort_R1+ base_mort_ER1 + base_mort_EV1+ base_mort_EVR1+
base_mort_QE1 + base_mort_QI1 + base_mort_QC1 + base_mort_QR1 +
base_mort_QEV1 + base_mort_QER1 + base_mort_QEVR1 + base_mort_QVR1 +
base_mort_H1+base_mort_HC1+base_mort_ICU1+base_mort_ICUC1+base_mort_ICUCV1+
base_mort_Vent1+base_mort_VentC1+base_mort_HCICU1+base_mort_HCV1)
results$death_treated_hospital <- round(cinc_mort_H1)
results$death_treated_icu <- round(cinc_mort_ICU1)
results$death_treated_ventilator <- round(cinc_mort_Vent1)
results$death_untreated_hospital <- round(cinc_mort_HC1)
results$death_untreated_icu <- round(cinc_mort_ICUC1)
results$death_untreated_ventilator <- round(cinc_mort_VentC1)+round(cinc_mort_ICUCV1)
results$attributable_deaths <- results$death_treated_hospital + results$death_treated_icu + results$death_treated_ventilator +
results$death_untreated_hospital + results$death_untreated_icu + results$death_untreated_ventilator
results$attributable_deaths_end <- last(results$attributable_deaths)
results$total_deaths <- results$attributable_deaths + results$death_natural_non_exposed + results$death_natural_exposed
results$total_deaths_end <- last(results$total_deaths)
results$total_reported_deaths_end <- last(results$cum_mortality)
# ## AGE DEPENDENT MORTALITY
cinc_mort_H1 <- parameters["nus"]*parameters["propo2"]*parameters["pdeath_ho"]*dexo2_hist*(out_mean[,(Hindex+1)]%*%ifr[,2])+
parameters["nus"]*(1-parameters["propo2"])*parameters["pdeath_h"]*(out_mean[,(Hindex+1)]%*%ifr[,2])
cinc_mort_HC1 <- parameters["nusc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_hco"]*(out_mean[,(HCindex+1)]%*%ifr[,2])+
parameters["nusc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_hc"]*(out_mean[,(HCindex+1)]%*%ifr[,2])
cinc_mort_ICU1 <- parameters["nu_icu"]*parameters["propo2"]*parameters["pdeath_icuo"]*dexo2_hist*(out_mean[,(ICUindex+1)]%*%ifr[,2])+
parameters["nu_icu"]*(1-parameters["propo2"])*parameters["pdeath_icu"]*(out_mean[,(ICUindex+1)]%*%ifr[,2])
cinc_mort_ICUC1 <- parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icuco"]*dexo2c_hist*(out_mean[,(ICUCindex+1)]%*%ifr[,2])+
parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icuc"]*(out_mean[,(ICUCindex+1)]%*%ifr[,2])
cinc_mort_Vent1 <- parameters["nu_vent"]*parameters["pdeath_vent"]*dexv_hist*(out_mean[,(Ventindex+1)]%*%ifr[,2])
cinc_mort_VentC1 <- parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_mean[,(VentCindex+1)]%*%ifr[,2])
cinc_mort_ICUCV1 <- parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_mean[,(ICUCVindex+1)]%*%ifr[,2])
cinc_mort_HCICU1 <- parameters["nu_icuc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_icu_hco"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])+
parameters["nu_icuc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_icu_hc"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])
cinc_mort_HCV1 <- parameters["nu_ventc"]*parameters["report_death_HC"]*parameters["pdeath_vent_hc"]*(out_mean[,(HCVindex+1)]%*%ifr[,2])
totage1<-as.data.frame(cinc_mort_H1+cinc_mort_HC1+cinc_mort_ICU1+cinc_mort_ICUC1+
cinc_mort_Vent1+cinc_mort_VentC1+cinc_mort_ICUCV1+cinc_mort_HCICU1+cinc_mort_HCV1)
basemort_H1<-(out_mean[,(Hindex+1)])
basemort_HC1<-(out_mean[,(HCindex+1)])
basemort_ICU1<-(out_mean[,(ICUindex+1)])
basemort_ICUC1<-(out_mean[,(ICUCindex+1)])
basemort_ICUCV1<-(out_mean[,(ICUCVindex+1)])
basemort_Vent1<-(out_mean[,(Ventindex+1)])
basemort_VentC1<-(out_mean[,(VentCindex+1)])
basemort_HCICU1<-(out_mean[,(HCICUindex+1)])
basemort_HCV1<-(out_mean[,(HCVindex+1)])
totbase1<-as.data.frame(basemort_H1+basemort_HC1+basemort_ICU1+basemort_ICUC1+basemort_ICUCV1+
basemort_Vent1+basemort_VentC1+basemort_HCICU1+basemort_HCV1)
tc<-c()
for (i in 1:dim(cinc_mort_H1)[1]) {
for (j in 1:dim(cinc_mort_H1)[2]) {
tc<-rbind(tc,c(i, j, totage1[i,j]*ifr[j,2]+totbase1[i,j]*mort[j]))
}
}
tc<-as.data.frame(tc)
colnames(tc)<-c("Day","Age","value")
results$tc <- tc %>%
mutate(Date = startdate + Day,
age_cat = case_when(
Age >= 1 & Age <= 6 ~ "<= 30 y.o.",
Age > 6 & Age <= 8 ~ "30-40 y.o.",
Age > 8 & Age <= 10 ~ "40-50 y.o.",
Age > 10 & Age <= 12 ~ "50-60 y.o.",
Age > 12 & Age <= 14 ~ "60-70 y.o.",
Age >= 15 ~ ">= 70 y.o.")) %>%
mutate(age_cat = factor(age_cat, levels = rev(c("<= 30 y.o.", "30-40 y.o.",
"40-50 y.o.", "50-60 y.o.", "60-70 y.o.", ">= 70 y.o."))))
mortality_lag <- data.frame(Age = popstruc$agefloor)
if(nrow(out_mean) >= 30) mortality_lag <- bind_cols(mortality_lag,
data.frame(day30 = out_mean[30,CMindex+1]/out_mean[30,Cindex+1]) %>%
mutate(day30 = ifelse(is.infinite(day30), 0, day30)))
if(nrow(out_mean) >= 60) mortality_lag <- bind_cols(mortality_lag,
data.frame(day60 = out_mean[60,CMindex+1]/out_mean[60,Cindex+1]) %>%
mutate(day60 = ifelse(is.infinite(day60), 0, day60)))
if(nrow(out_mean) >= 90) mortality_lag <- bind_cols(mortality_lag,
data.frame(day90 = out_mean[90,CMindex+1]/out_mean[90,Cindex+1]) %>%
mutate(day90 = ifelse(is.infinite(day90), 0, day90)))
if(nrow(out_mean) >= 120) mortality_lag <- bind_cols(mortality_lag,
data.frame(day120 = out_mean[120,CMindex+1]/out_mean[120,Cindex+1]) %>%
mutate(day120 = ifelse(is.infinite(day120), 0, day120)))
results$mortality_lag <- mortality_lag
if(iterations>1){
cinc_mort_1 <- cumsum(rowSums(parameters["nus"]*parameters["propo2"]*parameters["pdeath_ho"]*dexo2_hist*(out_min[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_2 <- cumsum(rowSums(parameters["nus"]*(1-parameters["propo2"])*parameters["pdeath_h"]*(out_min[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_3 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_hco"]*(out_min[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_4 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_hc"]*(out_min[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_5 <- cumsum(rowSums(parameters["nu_icu"]*parameters["propo2"]*parameters["pdeath_icuo"]*dexo2_hist*(out_min[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_6 <- cumsum(rowSums(parameters["nu_icu"]*(1-parameters["propo2"])*parameters["pdeath_icu"]*(out_min[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_7 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icuco"]*dexo2c_hist*(out_min[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_8 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icuc"]*(out_min[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_9 <- cumsum(rowSums(parameters["nu_vent"]*parameters["pdeath_vent"]*dexv_hist*(out_min[,(Ventindex+1)]%*%ifr[,2])))
cinc_mort_10 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_min[,(VentCindex+1)]%*%ifr[,2])))
cinc_mort_11 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_min[,(ICUCVindex+1)]%*%ifr[,2])))
cinc_mort_121 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icu_hco"]*(out_min[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_131 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icu_hc"]*(out_min[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_141 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_vent_hc"]*(out_min[,(HCVindex+1)]%*%ifr[,2])))
cinc_mort_all_min<-cinc_mort_1+cinc_mort_2+cinc_mort_3+cinc_mort_4+cinc_mort_5+cinc_mort_6+
cinc_mort_7+cinc_mort_8+cinc_mort_9+cinc_mort_10+cinc_mort_11+cinc_mort_121+cinc_mort_131+cinc_mort_141
cinc_mort_1 <- cumsum(rowSums(parameters["nus"]*parameters["propo2"]*parameters["pdeath_ho"]*dexo2_hist*(out_max[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_2 <- cumsum(rowSums(parameters["nus"]*(1-parameters["propo2"])*parameters["pdeath_h"]*(out_max[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_3 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_hco"]*(out_max[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_4 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_hc"]*(out_max[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_5 <- cumsum(rowSums(parameters["nu_icu"]*parameters["propo2"]*parameters["pdeath_icuo"]*dexo2_hist*(out_max[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_6 <- cumsum(rowSums(parameters["nu_icu"]*(1-parameters["propo2"])*parameters["pdeath_icu"]*(out_max[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_7 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icuco"]*dexo2c_hist*(out_max[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_8 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icuc"]*(out_max[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_9 <- cumsum(rowSums(parameters["nu_vent"]*parameters["pdeath_vent"]*dexv_hist*(out_max[,(Ventindex+1)]%*%ifr[,2])))
cinc_mort_10 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_max[,(VentCindex+1)]%*%ifr[,2])))
cinc_mort_11 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_max[,(ICUCVindex+1)]%*%ifr[,2])))
cinc_mort_121 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icu_hco"]*(out_max[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_131 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icu_hc"]*(out_max[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_141 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_vent_hc"]*(out_max[,(HCVindex+1)]%*%ifr[,2])))
cinc_mort_all_max<-cinc_mort_1+cinc_mort_2+cinc_mort_3+cinc_mort_4+cinc_mort_5+cinc_mort_6+
cinc_mort_7+cinc_mort_8+cinc_mort_9+cinc_mort_10+cinc_mort_11+cinc_mort_121+cinc_mort_131+cinc_mort_141
base_mort_H1 <- cumsum(rowSums(out_min[,(Hindex+1)]%*%mort))
base_mort_ICU1 <- cumsum(rowSums(out_min[,(ICUindex+1)]%*%mort))
base_mort_ICUC1 <- cumsum(rowSums(out_min[,(ICUCindex+1)]%*%mort))
base_mort_ICUCV1 <- cumsum(rowSums(out_min[,(ICUCVindex+1)]%*%mort))
base_mort_Vent1 <- cumsum(rowSums(out_min[,(Ventindex+1)]%*%mort))
base_mort_VentC1 <- cumsum(rowSums(out_min[,(VentCindex+1)]%*%mort))
base_mort_Z1 <- cumsum(rowSums(out_min[,(Zindex+1)]%*%mort))
base_mort_HC11 <- cumsum(rowSums(out_min[,(HCindex+1)]%*%mort))
base_mort_HCICU11 <- cumsum(rowSums(out_min[,(HCICUindex+1)]%*%mort))
base_mort_HCV11 <- cumsum(rowSums(out_min[,(HCVindex+1)]%*%mort))
base_mort_E11 <- cumsum(rowSums(out_min[,(Eindex+1)]%*%mort))
base_mort_I11 <- cumsum(rowSums(out_min[,(Iindex+1)]%*%mort))
base_mort_CL11 <- cumsum(rowSums(out_min[,(CLindex+1)]%*%mort))
base_mort_X11 <- cumsum(rowSums(out_min[,(Xindex+1)]%*%mort))
base_mort_QE11 <- cumsum(rowSums(out_min[,(QEindex+1)]%*%mort))
base_mort_QI11 <- cumsum(rowSums(out_min[,(QIindex+1)]%*%mort))
base_mort_QC11 <- cumsum(rowSums(out_min[,(QCindex+1)]%*%mort))
base_mort_ER11 <- cumsum(rowSums(out_min[,(ERindex+1)]%*%mort))
base_mort_EV11 <- cumsum(rowSums(out_min[,(EVindex+1)]%*%mort))
base_mort_EVR11 <- cumsum(rowSums(out_min[,(EVRindex+1)]%*%mort))
base_mort_QEV11 <- cumsum(rowSums(out_min[,(QEVindex+1)]%*%mort))
base_mort_QER11 <- cumsum(rowSums(out_min[,(QERindex+1)]%*%mort))
base_mort_QEVR11 <- cumsum(rowSums(out_min[,(QEVRindex+1)]%*%mort))
nat_deaths_inf_min <- last(round(base_mort_E11 + base_mort_I11 + base_mort_CL11 + base_mort_X11 +
base_mort_ER11 + base_mort_EV11+ base_mort_EVR11+
base_mort_QE11 + base_mort_QI11 + base_mort_QC11 +
base_mort_QEV11 + base_mort_QER11 + base_mort_QEVR11 + base_mort_Z1+
base_mort_H1+base_mort_HC11+base_mort_ICU1+base_mort_ICUC1+base_mort_ICUCV1+
base_mort_Vent1+base_mort_VentC1+base_mort_HCICU11+base_mort_HCV11))
base_mort_H1 <- cumsum(rowSums(out_max[,(Hindex+1)]%*%mort))
base_mort_ICU1 <- cumsum(rowSums(out_max[,(ICUindex+1)]%*%mort))
base_mort_ICUC1 <- cumsum(rowSums(out_max[,(ICUCindex+1)]%*%mort))
base_mort_ICUCV1 <- cumsum(rowSums(out_max[,(ICUCVindex+1)]%*%mort))
base_mort_Vent1 <- cumsum(rowSums(out_max[,(Ventindex+1)]%*%mort))
base_mort_VentC1 <- cumsum(rowSums(out_max[,(VentCindex+1)]%*%mort))
base_mort_Z1 <- cumsum(rowSums(out_max[,(Zindex+1)]%*%mort))
base_mort_HC11 <- cumsum(rowSums(out_max[,(HCindex+1)]%*%mort))
base_mort_HCICU11 <- cumsum(rowSums(out_max[,(HCICUindex+1)]%*%mort))
base_mort_HCV11 <- cumsum(rowSums(out_max[,(HCVindex+1)]%*%mort))
base_mort_E11 <- cumsum(rowSums(out_max[,(Eindex+1)]%*%mort))
base_mort_I11 <- cumsum(rowSums(out_max[,(Iindex+1)]%*%mort))
base_mort_CL11 <- cumsum(rowSums(out_max[,(CLindex+1)]%*%mort))
base_mort_X11 <- cumsum(rowSums(out_max[,(Xindex+1)]%*%mort))
base_mort_QE11 <- cumsum(rowSums(out_max[,(QEindex+1)]%*%mort))
base_mort_QI11 <- cumsum(rowSums(out_max[,(QIindex+1)]%*%mort))
base_mort_QC11 <- cumsum(rowSums(out_max[,(QCindex+1)]%*%mort))
base_mort_ER11 <- cumsum(rowSums(out_max[,(ERindex+1)]%*%mort))
base_mort_EV11 <- cumsum(rowSums(out_max[,(EVindex+1)]%*%mort))
base_mort_EVR11 <- cumsum(rowSums(out_max[,(EVRindex+1)]%*%mort))
base_mort_QEV11 <- cumsum(rowSums(out_max[,(QEVindex+1)]%*%mort))
base_mort_QER11 <- cumsum(rowSums(out_max[,(QERindex+1)]%*%mort))
base_mort_QEVR11 <- cumsum(rowSums(out_max[,(QEVRindex+1)]%*%mort))
nat_deaths_inf_max<- last(round(base_mort_E11 + base_mort_I11 + base_mort_CL11 + base_mort_X11 +
base_mort_ER11 + base_mort_EV11+ base_mort_EVR11+
base_mort_QE11 + base_mort_QI11 + base_mort_QC11 +
base_mort_QEV11 + base_mort_QER11 + base_mort_QEVR11 + base_mort_Z1+
base_mort_H1+base_mort_HC11+base_mort_ICU1+base_mort_ICUC1+base_mort_ICUCV1+
base_mort_Vent1+base_mort_VentC1+base_mort_HCICU11+base_mort_HCV11))
previcureq1_max<-rowSums(out_max[,(Hindex+1)])+ rowSums(out_max[,(ICUCindex+1)])+rowSums(out_max[,(ICUCVindex+1)]) # surge beds occupancy
previcureq21_max<-rowSums(out_max[,(ICUindex+1)])+rowSums(out_max[,(VentCindex+1)]) # icu beds occupancy
previcureq31_max<-rowSums(out_max[,(Ventindex+1)]) # ventilator occupancy
cmortality1_max<-rowSums(out_max[,(CMindex+1)]) # cumulative mortality
overloadH1_max<-rowSums(out_max[,(HCindex+1)]) # requirement for beds
overloadICU1_max<-rowSums(out_max[,(ICUCindex+1)])+ rowSums(out_max[,(HCICUindex+1)]) # requirement for icu beds
overloadICUV1_max<-rowSums(out_max[,(ICUCVindex+1)])+ rowSums(out_max[,(HCVindex+1)]) # requirement for ventilators
overloadVent1_max<-rowSums(out_max[,(VentCindex+1)]) # requirement for ventilators
ccases1_max<-rowSums(out_max[,(Cindex+1)]) # cumulative cases
reqsurge1_max<-rowSums(out_max[,(Hindex+1)])+overloadH1 # surge beds total requirements
reqicu1_max<-rowSums(out_max[,(ICUindex+1)])+overloadICU1 # ICU beds total requirements
reqvent1_max<-rowSums(out_max[,(Ventindex+1)])+overloadICUV1+overloadVent1 # ventilator beds total requirements
previcureq1_min<-rowSums(out_min[,(Hindex+1)])+rowSums(out_min[,(ICUCindex+1)])+rowSums(out_min[,(ICUCVindex+1)]) # surge beds occupancy
previcureq21_min<-rowSums(out_min[,(ICUindex+1)])+rowSums(out_min[,(VentCindex+1)]) # icu beds occupancy
previcureq31_min<-rowSums(out_min[,(Ventindex+1)]) # ventilator occupancy
cmortality1_min<-rowSums(out_min[,(CMindex+1)]) # cumulative mortality
overloadH1_min<-rowSums(out_min[,(HCindex+1)]) # requirement for beds
overloadICU1_min<-rowSums(out_min[,(ICUCindex+1)]) # requirement for icu beds
overloadICUV1_min<-rowSums(out_min[,(ICUCVindex+1)]) # requirement for ventilators
overloadVent1_min<-rowSums(out_min[,(VentCindex+1)]) # requirement for ventilators
ccases1_min<-rowSums(out_min[,(Cindex+1)]) # cumulative cases
reqsurge1_min<-rowSums(out_min[,(Hindex+1)])+overloadH1 # surge beds total requirements
reqicu1_min<-rowSums(out_min[,(ICUindex+1)])+overloadICU1 # ICU beds total requirements
reqvent1_min<-rowSums(out_min[,(Ventindex+1)])+overloadICUV1+overloadVent1 # ventilator beds total requirements
results$Rt_max <- out$max_Rt
results$Rt_min <- out$min_Rt
results$daily_incidence_max <- out$max_cases
results$daily_incidence_min <- out$min_cases
results$deaths_from_covid_min<-last(cinc_mort_all_min)
results$deaths_from_covid_max<-last(cinc_mort_all_max)
results$deaths_with_covid_min<-last(nat_deaths_inf_min)
results$deaths_with_covid_max<-last(nat_deaths_inf_max)
results$daily_total_cases_max <- out$max_daily_infection
results$daily_total_cases_min <- out$min_daily_infection
results$total_reported_deaths_end_min <- last(cmortality1_min)
results$total_reported_deaths_end_max <- last(cmortality1_max)
results$pct_total_pop_infected_min <- out$min_infections # proportion of the population that has been infected at the end of the simulation
results$pct_total_pop_infected_max <- out$max_infections # proportion of the population that has been infected at the end of the simulation
}
return(results)
}
multi_runs<-function(Y,times,parameters,input,iterations,noise,confidence,fit,fit_mat){
results <- list()
aux<-array(0, dim=c(length(times),35*A+1,iterations))
results$mean<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$min<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$max<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$mean_cases<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$min_cases<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$max_cases<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$mean_cum_cases<-matrix(0,nrow = length(times),ncol = 1)
results$min_cum_cases<-matrix(0,nrow = length(times),ncol = 1)
results$max_cum_cases<-matrix(0,nrow = length(times),ncol = 1)
results$mean_daily_infection<-matrix(0,nrow = length(times),ncol = 1)
results$min_daily_infection<-matrix(0,nrow = length(times),ncol = 1)
results$max_daily_infection<-matrix(0,nrow = length(times),ncol = 1)
results$mean_ab<-matrix(0,nrow = length(times),ncol = 1)
results$min_ab<-matrix(0,nrow = length(times),ncol = 1)
results$max_ab<-matrix(0,nrow = length(times),ncol = 1)
cases<-matrix(0, nrow=length(times),ncol=iterations)
cum_cases<-matrix(0, nrow=length(times),ncol=iterations)
day_infections<-matrix(0, nrow=length(times),ncol=iterations)
Rt_aux<-matrix(0, nrow=length(times),ncol=iterations)
infections<-matrix(0, nrow=iterations,ncol=1)
Rt <- NULL
print(iterations)
param_vector<-parameters
if(iterations>1){
for (i in 1:iterations){
print(i)
param_vector[parameters_noise]<-parameters[parameters_noise]+rnorm(length(parameters_noise),mean=0,sd=noise*abs(parameters[parameters_noise]))
if(fit){
param_vector[parameters_fit]<-fit_mat[,floor(runif(1)*(length(fit_mat[1,])-1))+1]
initE[aci]<-round(sum(popstruc[,2])/param_vector["init"])
initS<-popstruc[,2]-initE-initI-initCL-initR-initX-initZ-initV-initH-initHC-initICU-initICUC-initICUCV-initVent-initVentC-
initQS-initQE-initQI-initQR-initQC-initEV-initER-initEVR-initVR-initQV-initQEV-initQEVR-initQER-initQVR-
initHCICU-initHCV
Y<-c(initS,initE,initI,initR,initX,initH,initHC,initC,initCM,initV, initQS, initQE, initQI, initQR, initCL, initQC, initICU,
initICUC, initICUCV, initVent, initVentC, initCMC,initZ, initEV, initER, initEVR, initVR,
initQV,initQEV,initQEVR,initQER,initQVR,initHCICU,initHCV,initAb)
}
out0 <- ode(y = Y, times = times, method = "euler", hini = 0.05, func = covid, parms = param_vector, input=input)
aux[,,i]<-out0
critH<-c()
crit<-c()
critV<-c()
for (ii in 1:length(times)){
critH[ii]<-min(1-fH((sum(out0[ii,(Hindex+1)]))+sum(out0[ii,(ICUCindex+1)])+sum(out0[ii,(ICUCVindex+1)])),1)
crit[ii]<-min(1-fICU((sum(out0[ii,(ICUindex+1)]))+(sum(out0[ii,(Ventindex+1)]))+(sum(out0[ii,(VentCindex+1)]))),1)
critV[ii]<-min(1-fVent((sum(out0[ii,(Ventindex+1)]))),1)
}
# daily incidence
incidence<-param_vector["report"]*param_vector["gamma"]*(1-param_vector["pclin"])*out0[,(Eindex+1)]%*%(1-ihr[,2])+
param_vector["reportc"]*param_vector["gamma"]*param_vector["pclin"]*out0[,(Eindex+1)]%*%(1-ihr[,2])+
param_vector["report"]*param_vector["gamma"]*(1-param_vector["pclin"])*out0[,(QEindex+1)]%*%(1-ihr[,2])+
param_vector["reportc"]*param_vector["gamma"]*param_vector["pclin"]*out0[,(QEindex+1)]%*%(1-ihr[,2])+
param_vector["report_v"]*param_vector["gamma"]*(1-param_vector["pclin_v"])*out0[,(EVindex+1)]%*%(1-param_vector["sigmaEV"]*ihr[,2])+
param_vector["report_cv"]*param_vector["gamma"]*param_vector["pclin_v"]*out0[,(EVindex+1)]%*%(1-param_vector["sigmaEV"]*ihr[,2])+
param_vector["report_vr"]*param_vector["gamma"]*(1-param_vector["pclin_vr"])*out0[,(EVRindex+1)]%*%(1-param_vector["sigmaEVR"]*ihr[,2])+
param_vector["report_cvr"]*param_vector["gamma"]*param_vector["pclin_vr"]*out0[,(EVRindex+1)]%*%(1-param_vector["sigmaEVR"]*ihr[,2])+
param_vector["report_r"]*param_vector["gamma"]*(1-param_vector["pclin_r"])*out0[,(ERindex+1)]%*%(1-param_vector["sigmaER"]*ihr[,2])+
param_vector["report_cr"]*param_vector["gamma"]*param_vector["pclin_r"]*out0[,(ERindex+1)]%*%(1-param_vector["sigmaER"]*ihr[,2])
incidenceh<- param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu"])*param_vector["reporth"]+
param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu"])*(1-param_vector["reporth"])*param_vector["reporth_g"]+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu"])*param_vector["reporth"]+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu"])*(1-param_vector["reporth"])*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaEV"]*out0[,(EVindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu_v"])*param_vector["reporth"]+
param_vector["gamma"]*param_vector["sigmaEVR"]*out0[,(EVRindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu_vr"])*param_vector["reporth"]+
param_vector["gamma"]*param_vector["sigmaER"]*out0[,(ERindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu_r"])*param_vector["reporth"]+
param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*critH*param_vector["reporth_g"]*(1-param_vector["prob_icu"])+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*critH*param_vector["reporth_g"]*(1-param_vector["prob_icu"])+
param_vector["gamma"]*param_vector["sigmaEV"]*out0[,(EVindex+1)]%*%ihr[,2]*critH*param_vector["reporth_g"]*(1-param_vector["prob_icu_v"])+
param_vector["gamma"]*param_vector["sigmaEVR"]*out0[,(EVRindex+1)]%*%ihr[,2]*critH*param_vector["reporth_g"]*(1-param_vector["prob_icu_vr"])+
param_vector["gamma"]*param_vector["sigmaER"]*out0[,(ERindex+1)]%*%ihr[,2]*critH*param_vector["reporth_g"]*(1-param_vector["prob_icu_r"])+
#ICU
param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*(1-crit)*param_vector["reporth_ICU"]+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*(1-crit)*param_vector["reporth_ICU"]+
param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*crit*param_vector["reporth_ICU"]*param_vector["reporth_g"]+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*crit*param_vector["reporth_ICU"]*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaEV"]*out0[,(EVindex+1)]%*%ihr[,2]*(1-crit)*param_vector["prob_icu_v"]*param_vector["reporth_ICU"]+
param_vector["gamma"]*param_vector["sigmaEVR"]*out0[,(EVRindex+1)]%*%ihr[,2]*(1-crit)*param_vector["prob_icu_vr"]*param_vector["reporth_ICU"]+
param_vector["gamma"]*param_vector["sigmaER"]*out0[,(ERindex+1)]%*%ihr[,2]*(1-crit)*param_vector["prob_icu_r"]*param_vector["reporth_ICU"]+
param_vector["gamma"]*param_vector["sigmaEV"]*out0[,(EVindex+1)]%*%ihr[,2]*crit*param_vector["prob_icu_v"]*param_vector["reporth_ICU"]*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaEVR"]*out0[,(EVRindex+1)]%*%ihr[,2]*crit*param_vector["prob_icu_vr"]*param_vector["reporth_ICU"]*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaER"]*out0[,(ERindex+1)]%*%ihr[,2]*crit*param_vector["prob_icu_r"]*param_vector["reporth_ICU"]*param_vector["reporth_g"]+
param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*(1-param_vector["reporth_ICU"])*param_vector["reporth_g"]+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*(1-param_vector["reporth_ICU"])*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaEV"]*out0[,(EVindex+1)]%*%ihr[,2]*param_vector["prob_icu_v"]*(1-param_vector["reporth_ICU"])*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaEVR"]*out0[,(EVRindex+1)]%*%ihr[,2]*param_vector["prob_icu_vr"]*(1-param_vector["reporth_ICU"])*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaER"]*out0[,(ERindex+1)]%*%ihr[,2]*param_vector["prob_icu_r"]*(1-param_vector["reporth_ICU"])*param_vector["reporth_g"]
cases[,i]<-(rowSums(incidence)+rowSums(incidenceh)) # daily incidence cases
cum_cases[,i]<-colSums(incidence)+colSums(incidenceh) # cumulative incidence cases
day_infections[,i]<- round(rowSums(param_vector["gamma"]*out0[,(Eindex+1)]+
param_vector["gamma"]*out0[,(QEindex+1)]+
param_vector["gamma"]*out0[,(EVindex+1)]+
param_vector["gamma"]*out0[,(EVRindex+1)]+
param_vector["gamma"]*out0[,(ERindex+1)]))
# daily infections
infections[i] <- round(100*last(cumsum(day_infections[,i]))/sum(popstruc[,2]), 1) # proportion of the population that has been infected at the end of the simulation
for (w in (ceiling(1/param_vector["nui"])+1):length(times)){
Rt_aux[w,i]<-cumsum(sum(param_vector["gamma"]*out0[w,(Eindex+1)]))/cumsum(sum(param_vector["gamma"]*out0[(w-1/param_vector["nui"]),(Eindex+1)]))
if(Rt_aux[w,i] >= 7) {Rt_aux[w,i] <- NA}
}
}
qq <- quantile(infections, c(confidence, 0.5, (1-confidence)))
results$mean_infections<-qq[2]
results$min_infections<-qq[1]
results$max_infections<-qq[3]
for(i in 1:length(out0[,1])){
qq <- quantile(cases[i,], c(confidence, 0.5, (1-confidence)))
results$mean_cases[i]<-qq[2]
results$min_cases[i]<-qq[1]
results$max_cases[i]<-qq[3]
qq <- quantile(cum_cases[i,], c(confidence, 0.5, (1-confidence)))
results$mean_cum_cases[i]<-qq[2]
results$min_cum_cases[i]<-qq[1]
results$max_cum_cases[i]<-qq[3]
qq <- quantile(day_infections[i,], c(confidence, 0.5, (1-confidence)))
results$mean_daily_infection[i]<-qq[2]
results$min_daily_infection[i]<-qq[1]
results$max_daily_infection[i]<-qq[3]
qq <- quantile(Rt_aux[i,], c(confidence, 0.5, (1-confidence)),na.rm = T)
results$mean_Rt[i]<-qq[2]
results$min_Rt[i]<-qq[1]
results$max_Rt[i]<-qq[3]
for (j in 1:length(out0[1,])){
qq <- quantile(aux[i,j,], c(confidence, 0.5, (1-confidence)))
results$mean[i,j]<-qq[2]
results$min[i,j]<-qq[1]
results$max[i,j]<-qq[3]
}
}
}else{
results$mean <- ode(y = Y, times = times, method = "euler", hini = 0.05, func = covid, parms = parameters, input=input)
}
return(results)
}
out0<-multi_runs(Y, times, parameters, vectors0, iterations, noise, confidence,0,fit_mat)
out0$min_infections
out0$max_infections
plot(times,rowSums(out0$mean[,Iindex+1]),type = 'l')
polygon(c(times, rev(times)), c(rowSums(out0$max[,Iindex+1]), rev(rowSums(out0$min[,Iindex+1]))),
col=rgb(0, 0, 0,0.25), border = NA)
plot(times,out0$mean_Rt,type = 'l')
polygon(c(times, rev(times)), c(out0$max_Rt, rev(out0$min_Rt)),
col=rgb(0, 0, 0,0.25), border = NA)
simul_baseline <- process_ode_outcome(out0,iterations,vectors0)
# # write.csv(simul_baseline, paste0(hilo,"_baseline_",gsub(":|-","",Sys.time()),".csv"))
#
#future interventions
#extend travel ban, quarantine, hand washing, cocooning the elderly until 1st July
out <-multi_runs(Y, times, parameters, vectors, iterations, noise, confidence,0,fit_mat)
simul_interventions <- process_ode_outcome(out,iterations,vectors)
# write.csv(simul_interventions, paste0(hilo,"_futureIntv_",gsub(":|-","",Sys.time()),".csv"))
############# PLOTTING
# Fitting tab
# fitting the intervention lines to the data to account for any historical interventions
time<-as.Date(out0$mean[,1]+startdate)
par(mfrow=c(1,2))
# set up the axis limits
xmin<-min(as.Date(cases_rv[,1]))
xmax<-max(as.Date(cases_rv[,1]))
ymax<-max(cases_rv[,2],na.rm = T)
xtick<-seq(xmin, xmax, by=7)
plot(time,rowSums(simul_interventions$daily_incidence),type='l',lwd=3,
main="New Reported Cases", xlab="Date", ylab="Cases per day",
xlim=c(xmin,xmax), ylim=c(0,ymax), col='blue',xaxt="n")
axis(side=1, labels = FALSE)
text(x=xtick, y=-250, labels = format(xtick,"%b-%d"), srt = 0, xpd = TRUE)
points(as.Date(cases_rv[,1]),cases_rv[,2],pch=19,col='red')
# reset the maximum to the cumulative mortality
ymax<-max(cases_rv[,3],na.rm = T)
plot(time,simul_interventions$cum_mortality,type='l',lwd=3,
main="Cumulative Mortality", xlab="Date", ylab="Total deaths",
xlim=c(xmin,xmax), ylim=c(0,ymax), col='blue',xaxt="n")
text(x=xtick, y=-100, labels = format(xtick,"%b-%d"), srt = 0, xpd = TRUE)
points(as.Date(cases_rv[,1]),cases_rv[,3],pch=19,col='red')
### Predictions tab
par(mfrow=c(1,2))
### Cases at baseline and intervention
ymax<-max(c(cases_rv[,2],rowSums(simul_baseline$daily_incidence),rowSums(simul_interventions$daily_incidence)),na.rm=T)
plot(time,rowSums(simul_baseline$daily_incidence),type='l',lwd=3,col='blue',
main="Baseline", xlab="Date", ylab="New cases per day",ylim=c(0,ymax))
points(as.Date(cases_rv[,1]),cases_rv[,2],pch=19,col='red')
plot(time,rowSums(simul_interventions$daily_incidence),type='l',lwd=3,col='blue',
main="Intervention", xlab="Date", ylab="New cases per day",ylim=c(0,ymax))
points(as.Date(cases_rv[,1]),cases_rv[,2],pch=19,col='red')
# # # Hospital prevalences stratified by H,ICU and Vent
ymax<-max(c((simul_baseline$hospital_surge_beds+simul_baseline$icu_beds+simul_baseline$ventilators),(simul_interventions$hospital_surge_beds+simul_interventions$icu_beds+simul_interventions$ventilators)))
time<-as.Date(out0$mean[,1]+startdate)
coul=c("#047883", "#24A9E2","#051A46")
DM<-as.data.frame(cbind(time,simul_baseline$hospital_surge_beds,simul_baseline$icu_beds,simul_baseline$ventilators))
colnames(DM)<-c("Time","Hospital surge beds","ICU beds","Ventilators")
DM$Time<-as.Date(DM$Time,origin = "1970-01-01")
DMF<-melt(DM, id.vars="Time",measure.vars = c("Hospital surge beds","ICU beds","Ventilators"))
d0<-ggplot(DMF, aes(x = Time, y = value,fill=variable)) +
geom_area()+
scale_fill_manual(values=coul)
DM<-as.data.frame(cbind(time,simul_interventions$hospital_surge_beds,simul_interventions$icu_beds,simul_interventions$ventilators))
colnames(DM)<-c("Time","Hospital surge beds","ICU beds","Ventilators")
DM$Time<-as.Date(DM$Time, origin = "1970-01-01")
DMF<-melt(DM, id.vars="Time",measure.vars = c("Hospital surge beds","ICU beds","Ventilators"))
d1<-ggplot(DMF, aes(x = Time, y = value,fill=variable)) +
geom_area()+
scale_fill_manual(values=coul)
grid.arrange(d0+ylab("Number of Patients")+
ggtitle("Baseline")+
ylim(0, ymax)+
geom_hline(yintercept=(parameters["beds_available"]+parameters["icu_beds_available"]+parameters["ventilators_available"]), linetype="dashed", color = "#047883")+
geom_hline(yintercept=(parameters["icu_beds_available"]+parameters["ventilators_available"]), linetype="dashed", color = "#24A9E2")+
geom_hline(yintercept=parameters["ventilators_available"], linetype="dashed", color = "#051A46")+
theme_bw(),
d1+ylab("Number of Patients")+
ggtitle("Intervention")+
ylim(0, ymax)+
geom_hline(yintercept=(parameters["beds_available"]+parameters["icu_beds_available"]+parameters["ventilators_available"]), linetype="dashed", color = "#047883")+
geom_hline(yintercept=(parameters["icu_beds_available"]+parameters["ventilators_available"]), linetype="dashed", color = "#24A9E2")+
geom_hline(yintercept=parameters["ventilators_available"], linetype="dashed", color = "#051A46")+
theme_bw(),
nrow = 1)
# # # Cumulative mortality at baseline and intervention stratified by hospital status
ymax<-max(rowSums(cbind(simul_baseline$death_treated_hospital,
simul_baseline$death_treated_icu,
simul_baseline$death_treated_ventilator,
simul_baseline$death_untreated_hospital,
simul_baseline$death_untreated_icu,
simul_baseline$death_untreated_ventilator)), rowSums(cbind(simul_interventions$death_treated_hospital,
simul_interventions$death_treated_icu,
simul_interventions$death_treated_ventilator,
simul_interventions$death_untreated_hospital,
simul_interventions$death_untreated_icu,
simul_interventions$death_untreated_ventilator)))
time<-as.Date(out$mean[,1]+startdate)
coul=c("#047883", "#24A9E2","#051A46","#E68029", "#D63304","#D1D604")
DM0<-as.data.frame(cbind(time,
simul_baseline$death_treated_hospital,
simul_baseline$death_treated_icu,
simul_baseline$death_treated_ventilator,
simul_baseline$death_untreated_hospital,
simul_baseline$death_untreated_icu,
simul_baseline$death_untreated_ventilator))
colnames(DM0)<-c("Time", "Treated: Hospital","Treated: ICU","Treated: Ventilator","Untreated: Hospital","Untreated: ICU","Untreated: Ventilator")
DM0$Time<-as.Date(DM0$Time, origin = "1970-01-01")
DMF0<-melt(DM0, id.vars="Time",measure.vars = c("Treated: Hospital","Treated: ICU","Treated: Ventilator","Untreated: Hospital","Untreated: ICU","Untreated: Ventilator"))
m0<-ggplot(DMF0, aes(x = Time, y = value,fill=variable)) +
geom_area()
DM<-as.data.frame(cbind(time,
simul_interventions$death_treated_hospital,
simul_interventions$death_treated_icu,
simul_interventions$death_treated_ventilator,
simul_interventions$death_untreated_hospital,
simul_interventions$death_untreated_icu,
simul_interventions$death_untreated_ventilator))
colnames(DM)<-c("Time","Treated: Hospital","Treated: ICU","Treated: Ventilator","Untreated: Hospital","Untreated: ICU","Untreated: Ventilator")
DM$Time<-as.Date(DM$Time, origin = "1970-01-01")
DMF<-melt(DM, id.vars="Time",measure.vars = c("Treated: Hospital","Treated: ICU","Treated: Ventilator","Untreated: Hospital","Untreated: ICU","Untreated: Ventilator"))
m1<-ggplot(DMF, aes(x = Time, y = value,fill=variable)) +
geom_area()
grid.arrange(m0+ylab("Cumulatice mortality")+
ggtitle("Baseline")+
ylim(0, ymax),
m1+ylab("Cumulatice mortality")+
ggtitle("Intervention")+
ylim(0, ymax),
nrow = 1)
# Estimated basic reproduction number, R_t
par(mfrow=c(1,2))
ymax<-max(c(simul_baseline$Rt[!is.na(simul_baseline$Rt)],simul_interventions$Rt[!is.na(simul_interventions$Rt)]))
plot(time,simul_baseline$Rt,type='l',lwd=3,col='black',
main="Baseline", xlab="Date", ylab="Reproduction number",ylim=c(0,ymax))
lines(time,simul_baseline$Rt/simul_baseline$Rt,lwd=2,col='grey')
plot(time,simul_interventions$Rt,type='l',lwd=3,col='black',
main="Intervention", xlab="Date", ylab="Reproduction number",ylim=c(0,ymax))
lines(time,simul_interventions$Rt/simul_interventions$Rt,lwd=2,col='grey')
## AGE DEPENDENT MORTALITY
cinc_mort_H1 <- parameters["nus"]*parameters["pdeath_h"]*(out$mean[,(Hindex+1)])
cinc_mort_HC1 <- parameters["nusc"]*parameters["pdeath_hc"]*(out$mean[,(HCindex+1)])
cinc_mort_ICU1 <- parameters["nu_icu"]*parameters["pdeath_icu"]*out$mean[,(ICUindex+1)]
cinc_mort_ICUC1 <- parameters["nu_icuc"]*parameters["pdeath_icuc"]*out$mean[,(ICUCindex+1)]
cinc_mort_Vent1 <- parameters["nu_vent"]*parameters["pdeath_vent"]*out$mean[,(Ventindex+1)]
cinc_mort_VentC1 <- parameters["nu_ventc"]*parameters["pdeath_ventc"]*out$mean[,(VentCindex+1)]
totage1<-as.data.frame(cinc_mort_H1+cinc_mort_HC1+cinc_mort_ICU1+cinc_mort_ICUC1+cinc_mort_Vent1+cinc_mort_VentC1)
basemort_H1<-(out$mean[,(Hindex+1)])
basemort_HC1<-(out$mean[,(HCindex+1)])
basemort_ICU1<-(out$mean[,(ICUindex+1)])
basemort_ICUC1<-(out$mean[,(ICUCindex+1)])
basemort_Vent1<-(out$mean[,(Ventindex+1)])
basemort_VentC1<-(out$mean[,(VentCindex+1)])
totbase1<-as.data.frame(basemort_H1+basemort_HC1+basemort_ICU1+basemort_ICUC1+basemort_Vent1+basemort_VentC1)
tc<-c()
ages<-seq(0,100,by=5)
for (i in 1:dim(cinc_mort_H1)[1]) {
for (j in 1:dim(cinc_mort_H1)[2]) {
tc<-rbind(tc,c(i,ages[j],totage1[i,j]*ifr[j,2]+totbase1[i,j]*mort[j]))
}
}
tc<-as.data.frame(tc)
colnames(tc)<-c("Day","Age","value")
tc$Age<-as.factor(tc$Age)
p6<-ggplot(data=tc, aes(x=Day,y=value,fill=Age))+
geom_bar(stat = "identity",position="fill", width=1)+
ylab("Proportion of deaths")
inc_mort_H0 <- parameters["nus"]*parameters["pdeath_h"]*(out0$mean[,(Hindex+1)])
inc_mort_HC0 <- parameters["nusc"]*parameters["pdeath_hc"]*(out0$mean[,(HCindex+1)])
inc_mort_ICU0 <- parameters["nu_icu"]*parameters["pdeath_icu"]*out0$mean[,(ICUindex+1)]
inc_mort_ICUC0 <- parameters["nu_icuc"]*parameters["pdeath_icuc"]*out0$mean[,(ICUCindex+1)]
inc_mort_Vent0 <- parameters["nu_vent"]*parameters["pdeath_vent"]*out0$mean[,(Ventindex+1)]
inc_mort_VentC0 <- parameters["nu_ventc"]*parameters["pdeath_ventc"]*out0$mean[,(VentCindex+1)]
totage0<-as.data.frame(inc_mort_H0+inc_mort_HC0+inc_mort_ICU0+inc_mort_ICUC0+inc_mort_Vent0+inc_mort_VentC0)
basemort_H0<-(out0$mean[,(Hindex+1)])
basemort_HC0<-(out0$mean[,(HCindex+1)])
basemort_ICU0<-(out0$mean[,(ICUindex+1)])
basemort_ICUC0<-(out0$mean[,(ICUCindex+1)])
basemort_Vent0<-(out0$mean[,(Ventindex+1)])
basemort_VentC0<-(out0$mean[,(VentCindex+1)])
totbase0<-as.data.frame(basemort_H0+basemort_HC0+basemort_ICU0+basemort_ICUC0+basemort_Vent0+basemort_VentC0)
tc0<-c()
for (i in 1:dim(cinc_mort_H1)[1]) {
for (j in 1:dim(cinc_mort_H1)[2]) {
tc0<-rbind(tc0,c(i,ages[j],totage0[i,j]*ifr[j,2]+totbase0[i,j]*mort[j]))
}
}
tc0<-as.data.frame(tc0)
colnames(tc0)<-c("Day","Age","value")
tc0$Age<-as.factor(tc0$Age)
p16<-ggplot(data=tc0, aes(x=Day,y=value,fill=Age))+
geom_bar(stat = "identity",position="fill", width=1)+ylab("Proportion of deaths")
grid.arrange(p16+theme_minimal(),
p6+theme_minimal(),
nrow=1)
####### PLOT Abs
samp.sizes<-round(rnorm(length(times),parameters["sample_size"],parameters["sample_size"]/5))
ab0<-as.data.frame(matrix(0,nrow=length(times)*1000,ncol=2))
colnames(ab0)<-c("Time","Ab")
ab0$Time<-rep(times,1000)
aux<-c()
for (i in 1:1000) {
num.inf.samp <- rbinom(length(times), size = samp.sizes, prob = (simul_baseline$ab_all_ages/simul_baseline$N))
aux<-c(aux,num.inf.samp/samp.sizes)
}
ab0$Ab<-aux
ggplot(ab0,aes(x=Time,y=Ab,group=Time))+geom_boxplot()
samp.sizes<-round(rnorm(length(times),parameters["sample_size"],parameters["sample_size"]/5))
ab<-as.data.frame(matrix(0,nrow=length(times)*1000,ncol=2))
colnames(ab)<-c("Time","Ab")
ab$Time<-rep(times,1000)
aux<-c()
for (i in 1:1000) {
num.inf.samp <- rbinom(length(times), size = samp.sizes, prob = (simul_interventions$ab_all_ages/simul_interventions$N))
aux<-c(aux,num.inf.samp/samp.sizes)
}
se<-0.5
sp<-0.9
ab$Ab<-se*aux+(1-sp)*(1-aux)
ggplot(ab,aes(x=Time,y=Ab,group=Time))+geom_boxplot()
##########################################################################################################################
###### SUMMARY METRICS ################################################################################################
########################################################################################################################
infected0<-tail((rowSums(out0$mean[,(Rindex+1)])),1)/sum(popstruc[,2])
infected0
infected1<-tail((rowSums(out$mean[,(Rindex+1)])),1)/sum(popstruc[,2])
infected1
ocelhay/como documentation built on April 18, 2023, 10:29 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
require("deSolve")
library("ggplot2")
library("dplyr")
library("reshape2")
require(gridExtra)
library(ggpubr)
library(bsplus)
library(deSolve)
library(DT)
library(highcharter)
library(lubridate)
library(pushbar)
library(readxl)
library(reshape2)
library(scales)
library(shiny)
library(shinyBS)
library(shinycssloaders)
library(shinyhelper)
library(shinythemes)
library(shinyWidgets)
library(tidyverse)
library(XLConnect)
library(stringr)
library(RColorBrewer)
# library("comoOdeCpp")
#read data from excel file
setwd("C:/covid19/covid_age")
load("data_CoMo.RData")
file_path <- paste0(getwd(),"/Template_CoMoCOVID-19App.xlsx")
country_name<-"United Kingdom of Great Britain"
fit_mat <- read.table("fit_mat.txt",header = T)
# Cases
dta <- read_excel(file_path, sheet = "Cases")
names(dta) <- c("date", "cases", "deaths","rep_cases","rep_deaths")
cases_rv <- dta %>%
mutate(date = as.Date(date), cumulative_death = cumsum(deaths)) %>%
as.data.frame()
# Severity/Mortality
dta <- read_excel(file_path, sheet = "Severity-Mortality")
names(dta) <- c("age_category", "ifr", "ihr")
mort_sever_rv <- dta %>%
mutate(ihr = ihr/100) %>% # starting unit should be % - scaling to a value between 0 and 1
mutate(ifr = ifr/max(ifr)) # scaling to a value between 0 and 1
# Population
dta <- read_excel(file_path, sheet = "Population")
names(dta) <- c("age_category", "pop", "birth", "death")
population_rv <- dta %>%
transmute(age_category, pop, birth, death)
# Parameters
param <- bind_rows(read_excel(file_path, sheet = "Parameters"),
read_excel(file_path, sheet = "Country Area Param"),
read_excel(file_path, sheet = "Virus Param"),
read_excel(file_path, sheet = "Hospitalisation Param"),
read_excel(file_path, sheet = "Interventions Param"),
read_excel(file_path, sheet = "Interventions")) %>%
mutate(Value_Date = as.Date(Value_Date))
# START Bridge ----
popstruc <- population_rv %>%
select(age_category, pop) %>%
rename(agefloor = age_category) %>%
as.data.frame()
popbirth <- population_rv %>%
select(age_category, birth) %>%
as.data.frame() # unit should be per person per day
mort <- population_rv %>%
pull(death) # unit should be per person per day
ihr <- mort_sever_rv %>%
select(age_category, ihr) %>%
as.data.frame()
ifr <- mort_sever_rv %>%
select(age_category, ifr) %>%
as.data.frame()
######### POP AGEING
# per year ageing matrix
A<-length(popstruc[,2])
dd<-seq(1:A)/seq(1:A)
ageing <- t(diff(diag(dd),lag=1)/(5*365.25))
ageing<-cbind(ageing,0*seq(1:A)) # no ageing from last compartment
#
# pop<-population$country==country_name
# pp<-population$pop[pop]
### CONTACT MATRICES
c_home <- contact_home[[country_name]] %>% as.matrix()
c_school <- contact_school[[country_name]] %>% as.matrix()
c_work <- contact_work[[country_name]] %>% as.matrix()
c_other <- contact_other[[country_name]] %>% as.matrix()
nce <-A-length(c_home[1,])
contact_home<-matrix(0,nrow=A,ncol=A)
contact_school<-matrix(0,nrow=A,ncol=A)
contact_work<-matrix(0,nrow=A,ncol=A)
contact_other<-matrix(0,nrow=A,ncol=A)
for (i in 1:(A-nce)){
for (j in 1:(A-nce)){
contact_home[i,j]<-c_home[i,j]
contact_school[i,j]<-c_school[i,j]
contact_work[i,j]<-c_work[i,j]
contact_other[i,j]<-c_other[i,j]
}
}
for (i in (A+1-nce):A){
for (j in 1:(A-nce)){
contact_home[i,j]<-c_home[(A-nce),j]
contact_school[i,j]<-c_school[(A-nce),j]
contact_work[i,j]<-c_work[(A-nce),j]
contact_other[i,j]<-c_other[(A-nce),j]
}
}
for (i in 1:(A-nce)){
for (j in (A+1-nce):A){
contact_home[i,j]<-c_home[i,(A-nce)]
contact_school[i,j]<-c_school[i,(A-nce)]
contact_work[i,j]<-c_work[i,(A-nce)]
contact_other[i,j]<-c_other[i,(A-nce)]
}
}
for (i in (A+1-nce):A){
for (j in (A+1-nce):A){
contact_home[i,j]<-c_home[(A-nce),(A-nce)]
contact_school[i,j]<-c_school[(A-nce),(A-nce)]
contact_work[i,j]<-c_work[(A-nce),(A-nce)]
contact_other[i,j]<-c_other[(A-nce),(A-nce)]
}
}
######### INITIALISE SIMULATION/INTERVENTION START TIMES
startdate <- param$Value_Date[param$Parameter == "date_range_simul_start"]
stopdate <- param$Value_Date[param$Parameter == "date_range_simul_end"]
startdate <- startdate[1]
stopdate <- stopdate[1]
day_start <- as.numeric(startdate-startdate)
day_stop <- as.numeric(stopdate-startdate)
times <- seq(day_start, day_stop)
tin<-as.numeric(startdate-as.Date("2020-01-01"))/365.25
initP<-sum(popstruc[,2]) # population size
ageindcase<-20 # age of index case (years)
aci <- floor((ageindcase/5)+1) # age class of index case
############# DEFINE PARAMETERS
parameters <- c(
### Transmission instrinsic
p = param$Value[param$Parameter=="p"][1],
rho = param$Value[param$Parameter=="rho"][1],
omega = param$Value[param$Parameter=="omega"][1],
gamma = param$Value[param$Parameter=="gamma"][1],
nui = param$Value[param$Parameter=="nui"][1],
report = param$Value[param$Parameter=="report"][1],
reportc = param$Value[param$Parameter=="reportc"][1],
reporth = param$Value[param$Parameter=="reporth"][1],
beds_available = param$Value[param$Parameter=="beds_available"][1],
icu_beds_available = param$Value[param$Parameter=="icu_beds_available"][1],
ventilators_available = param$Value[param$Parameter=="ventilators_available"][1],
give = 95,
pdeath_h = mean( param$Value[param$Parameter=="pdeath_h"],na.rm=T),
pdeath_ho = mean( param$Value[param$Parameter=="pdeath_ho"],na.rm=T),
pdeath_hc = mean( param$Value[param$Parameter=="pdeath_hc"],na.rm=T),
pdeath_hco = mean( param$Value[param$Parameter=="pdeath_hco"],na.rm=T),
pdeath_icu = mean( param$Value[param$Parameter=="pdeath_icu"],na.rm=T),
pdeath_icuo = mean( param$Value[param$Parameter=="pdeath_icuo"],na.rm=T),
pdeath_icuc = mean( param$Value[param$Parameter=="pdeath_icuc"],na.rm=T),
pdeath_icuco = mean( param$Value[param$Parameter=="pdeath_icuco"],na.rm=T),
pdeath_vent = mean( param$Value[param$Parameter=="pdeath_vent"],na.rm=T),
pdeath_ventc = mean( param$Value[param$Parameter=="pdeath_ventc"],na.rm=T),
ihr_scaling = param$Value[param$Parameter=="ihr_scaling"][1],
nus = param$Value[param$Parameter=="nus"][1],
nusc = param$Value[param$Parameter=="nus"][1], # nusc = nus
nu_icu = param$Value[param$Parameter=="nu_icu"][1],
nu_icuc = param$Value[param$Parameter=="nu_icu"][1], # nu_icuc = nu_icu
nu_vent = param$Value[param$Parameter=="nu_vent"][1],
nu_ventc = param$Value[param$Parameter=="nu_vent"][1], # nu_ventc = nu_vent
rhos = param$Value[param$Parameter=="rhos"][1],
amp = param$Value[param$Parameter=="amp"][1],
phi = param$Value[param$Parameter=="phi"][1],
pclin = param$Value[param$Parameter=="pclin"][1],
prob_icu = param$Value[param$Parameter=="prob_icu"][1],
prob_vent = param$Value[param$Parameter=="prob_vent"][1],
propo2 = param$Value[param$Parameter=="propo2"][1],
dexo2 = mean( param$Value[param$Parameter=="dexo2"],na.rm=T),
dexo2c = mean( param$Value[param$Parameter=="dexo2c"],na.rm=T),
dexv = mean( param$Value[param$Parameter=="dexvc"],na.rm=T),
dexvc = mean( param$Value[param$Parameter=="dexvc"],na.rm=T),
vent_dex = mean(param$Value[param$Parameter=="vent_dex"],na.rm=T),
prob_icu_v = mean(param$Value[param$Parameter=="prob_icu_v"],na.rm=T),
prob_icu_vr = mean(param$Value[param$Parameter=="prob_icu_vr"],na.rm=T),
prob_icu_r = mean(param$Value[param$Parameter=="prob_icu_r"],na.rm=T),
prob_v_v = mean(param$Value[param$Parameter=="prob_v_v"],na.rm=T),
prob_v_vr = mean(param$Value[param$Parameter=="prob_v_vr"],na.rm=T),
prob_v_r = mean(param$Value[param$Parameter=="prob_v_r"],na.rm=T),
pclin_v = mean(param$Value[param$Parameter=="pclin_v"],na.rm=T),
pclin_vr = mean(param$Value[param$Parameter=="pclin_vr"],na.rm=T),
pclin_r = mean(param$Value[param$Parameter=="pclin_r"],na.rm=T),
sigmaEV = mean(param$Value[param$Parameter=="sigmaEV"],na.rm=T),
sigmaEVR = mean(param$Value[param$Parameter=="sigmaEVR"],na.rm=T),
sigmaER = mean(param$Value[param$Parameter=="sigmaER"],na.rm=T),
sigmaR = mean(param$Value[param$Parameter=="sigmaR"],na.rm=T),
vac_dur = mean(param$Value[param$Parameter=="vac_dur"],na.rm=T),
vac_dur_r = mean(param$Value[param$Parameter=="vac_dur_r"],na.rm=T),
report_natdeathI = mean(param$Value[param$Parameter=="report_natdeathI"],na.rm=T),
report_natdeathCL = mean(param$Value[param$Parameter=="report_natdeathCL"],na.rm=T),
pre = mean(param$Value[param$Parameter=="pre"],na.rm=T),
report_v = param$Value[param$Parameter=="report_v"][1],
report_cv = param$Value[param$Parameter=="report_cv"][1],
report_vr = param$Value[param$Parameter=="report_vr"][1],
report_cvr = param$Value[param$Parameter=="report_cvr"][1],
report_r = param$Value[param$Parameter=="report_r"][1],
report_cr = param$Value[param$Parameter=="report_cr"][1],
reporth_ICU = param$Value[param$Parameter=="reporth_ICU"][1],
report_death_HC = param$Value[param$Parameter=="report_death_HC"][1],
pdeath_vent_hc = mean( param$Value[param$Parameter=="pdeath_vent_hc"],na.rm=T),
pdeath_icu_hc = mean( param$Value[param$Parameter=="pdeath_icu_hc"],na.rm=T),
pdeath_icu_hco = mean( param$Value[param$Parameter=="pdeath_icu_hco"],na.rm=T),
reporth_g = param$Value[param$Parameter=="reporth_g"][1],
seroneg = param$Value[param$Parameter=="seroneg"][1],
sample_size = param$Value[param$Parameter=="sample_size"][1],
### INTERVENTIONS
# self isolation
selfis_eff = mean(param$Value[param$Parameter=="selfis_eff"],na.rm=T),
# social distancing
dist_eff = mean(param$Value[param$Parameter=="dist_eff"],na.rm=T),
# hand washing
hand_eff = mean(param$Value[param$Parameter=="hand_eff"],na.rm=T),
# mask wearing
mask_eff = mean(param$Value[param$Parameter=="mask_eff"],na.rm=T),
# working at home
work_eff = mean(param$Value[param$Parameter=="work_eff"],na.rm=T),
w2h = mean(param$Value[param$Parameter=="w2h"],na.rm=T),
# school closures
school_eff = mean(param$Value[param$Parameter=="school_eff"],na.rm=T),
s2h = mean(param$Value[param$Parameter=="s2h"],na.rm=T),
# cocooning the elderly
cocoon_eff = mean(param$Value[param$Parameter=="cocoon_eff"],na.rm=T),
age_cocoon = mean(param$Value[param$Parameter=="age_cocoon"],na.rm=T),
# vaccination campaign
# vaccine_on = as.numeric(param$Value_Date[param$Parameter=="date_vaccine_on"] - startdate),
vaccine_eff = mean(param$Value[param$Parameter=="vaccine_eff"],na.rm=T),
vaccine_eff_r = mean(param$Value[param$Parameter=="vaccine_eff_r"],na.rm=T),
age_vaccine_min = mean(param$Value[param$Parameter=="age_vaccine_min"],na.rm=T),
age_vaccine_max = mean(param$Value[param$Parameter=="age_vaccine_max"],na.rm=T),
# vaccine_cov = param$Value[param$Parameter=="vaccine_cov"],
vac_campaign = mean(param$Value[param$Parameter=="vac_campaign"],na.rm=T),
# travel ban
mean_imports = mean(param$Value[param$Parameter=="mean_imports"],na.rm=T),
# screening
screen_test_sens = mean(param$Value[param$Parameter=="screen_test_sens"],na.rm=T),
# screen_contacts = mean(param$Value[param$Parameter=="screen_contacts"],na.rm=T),
screen_overdispersion = mean(param$Value[param$Parameter=="screen_overdispersion"],na.rm=T),
# voluntary home quarantine
quarantine_days = mean(param$Value[param$Parameter=="quarantine_days"],na.rm=T),
quarantine_effort = mean(param$Value[param$Parameter=="quarantine_effort"],na.rm=T),
quarantine_eff_home = mean(param$Value[param$Parameter=="quarantine_eff_home"],na.rm=T),
quarantine_eff_other = mean(param$Value[param$Parameter=="quarantine_eff_other"],na.rm=T),
# mass testing
age_testing_min = mean(param$Value[param$Parameter=="age_testing_min"],na.rm=T),
age_testing_max = mean(param$Value[param$Parameter=="age_testing_max"],na.rm=T),
mass_test_sens = mean(param$Value[param$Parameter=="mass_test_sens"],na.rm=T),
isolation_days = mean(param$Value[param$Parameter=="isolation_days"],na.rm=T),
### Initialisation
init = param$Value[param$Parameter=="init"][1],
### Others
household_size = param$Value[param$Parameter=="household_size"][1],
noise = param$Value[param$Parameter=="noise"][1],
iterations = param$Value[param$Parameter=="iterations"][1],
confidence = param$Value[param$Parameter=="confidence"][1]
)
ihr[,2]<- parameters["ihr_scaling"]*ihr[,2]
parameters["ifr_correction_young"]<-1
parameters["ifr_correction_old"]<-1
# ifr[1:12,2]<-ifr[1:12,2]/ifr_correction_young
# ihr$ihr[15:21]<-ihr$ihr[15:21]*ifr_correction_old
# Scale parameters to percentages/ rates
parameters["rho"]<-parameters["rho"]/100
parameters["omega"]<-(1/(parameters["omega"]*365))
parameters["gamma"]<-1/parameters["gamma"]
parameters["nui"]<-1/parameters["nui"]
parameters["report"]<-parameters["report"]/100
parameters["reportc"]<-parameters["reportc"]/100
parameters["report_v"]<-parameters["report_v"]/100
parameters["report_cv"]<-parameters["report_cv"]/100
parameters["report_vr"]<-parameters["report_vr"]/100
parameters["report_cvr"]<-parameters["report_cvr"]/100
parameters["report_r"]<-parameters["report_r"]/100
parameters["report_cr"]<-parameters["report_cr"]/100
parameters["reporth"]<-parameters["reporth"]/100
parameters["nus"]<-1/parameters["nus"]
parameters["rhos"]<-parameters["rhos"]/100
parameters["amp"]<-parameters["amp"]/100
parameters["selfis_eff"]<-parameters["selfis_eff"]/100
parameters["dist_eff"]<-parameters["dist_eff"]/100
parameters["hand_eff"]<-parameters["hand_eff"]/100
parameters["mask_eff"]<-parameters["mask_eff"]/100
parameters["work_eff"]<-parameters["work_eff"]/100
parameters["w2h"]<-parameters["w2h"]/100
parameters["school_eff"]<-parameters["school_eff"]/100
parameters["s2h"]<-parameters["s2h"]/100
parameters["cocoon_eff"]<-parameters["cocoon_eff"]/100
parameters["age_cocoon"]<-floor((parameters["age_cocoon"]/5)+1)
parameters["vaccine_eff"]<-parameters["vaccine_eff"]/100
parameters["vaccine_eff_r"]<-parameters["vaccine_eff_r"]/100
age_vaccine_min<-(parameters["age_vaccine_min"])
age_vaccine_max<-(parameters["age_vaccine_max"])
# parameters["vaccine_cov"]<-parameters["vaccine_cov"]/100
# parameters["vac_campaign"]<-parameters["vac_campaign"]*7
parameters["screen_test_sens"]<-parameters["screen_test_sens"]/100
parameters["quarantine_days"]<-parameters["quarantine_days"]
parameters["quarantine_effort"]<-1/parameters["quarantine_effort"]
parameters["quarantine_eff_home"]<-parameters["quarantine_eff_home"]/-100
parameters["quarantine_eff_other"]<-parameters["quarantine_eff_other"]/100
parameters["give"]<-parameters["give"]/100
parameters["pdeath_h"]<-parameters["pdeath_h"]/100
parameters["pdeath_ho"]<-parameters["pdeath_ho"]/100
parameters["pdeath_hc"]<-parameters["pdeath_hc"]/100
parameters["pdeath_hco"]<-parameters["pdeath_hco"]/100
parameters["pdeath_icu"]<-parameters["pdeath_icu"]/100
parameters["pdeath_icuo"]<-parameters["pdeath_icuo"]/100
parameters["pdeath_icuc"]<-parameters["pdeath_icuc"]/100
parameters["pdeath_icuco"]<-parameters["pdeath_icuco"]/100
parameters["pdeath_vent"]<-parameters["pdeath_vent"]/100
parameters["pdeath_ventc"]<-parameters["pdeath_ventc"]/100
parameters["nusc"]<-1/parameters["nusc"]
parameters["nu_icu"]<-1/parameters["nu_icu"]
parameters["nu_icuc"]<-1/parameters["nu_icuc"]
parameters["nu_vent"]<-1/parameters["nu_vent"]
parameters["nu_ventc"]<-1/parameters["nu_ventc"]
parameters["pclin"]<-parameters["pclin"]/100
parameters["prob_icu"]<-parameters["prob_icu"]/100
parameters["prob_vent"]<-parameters["prob_vent"]/100
iterations<-parameters["iterations"]
noise<-parameters["noise"]
confidence<-parameters["confidence"]/100
parameters["mass_test_sens"]<-parameters["mass_test_sens"]/100
age_testing_min<-(parameters["age_testing_min"])
age_testing_max<-(parameters["age_testing_max"])
parameters["isolation_days"]<-parameters["isolation_days"]
parameters["propo2"]<-parameters["propo2"]/100
parameters["dexo2"]<-parameters["dexo2"]/100
parameters["dexo2c"]<-parameters["dexo2c"]/100
parameters["dexv"]<-parameters["dexv"]/100
parameters["dexvc"]<-parameters["dexvc"]/100
parameters["vent_dex"]<-parameters["vent_dex"]/100
parameters["prob_icu_v"]<-parameters["prob_icu_v"]/100
parameters["prob_icu_vr"]<-parameters["prob_icu_vr"]/100
parameters["prob_icu_r"]<-parameters["prob_icu_r"]/100
parameters["prob_v_v"]<-parameters["prob_v_v"]/100
parameters["prob_v_r"]<-parameters["prob_v_r"]/100
parameters["prob_v_vr"]<-parameters["prob_v_vr"]/100
parameters["pclin_v"]<-parameters["pclin_v"]/100
parameters["pclin_vr"]<-parameters["pclin_vr"]/100
parameters["pclin_r"]<-parameters["pclin_r"]/100
parameters["sigmaEV"]<-parameters["sigmaEV"]/100
parameters["sigmaER"]<-parameters["sigmaER"]/100
parameters["sigmaEVR"]<-parameters["sigmaEVR"]/100
parameters["sigmaR"]<-parameters["sigmaR"]/100
parameters["vac_dur"]<-1/parameters["vac_dur"]/100
parameters["vac_dur_r"]<-1/parameters["vac_dur_r"]/100
parameters["report_natdeathI"]<-parameters["report_natdeathI"]/100
parameters["report_natdeathCL"]<-parameters["report_natdeathCL"]/100
parameters["report_death_HC"]<-parameters["report_death_HC"]/100
parameters["reporth_ICU"]<-parameters["reporth_ICU"]/100
parameters["pre"]<-parameters["pre"]/100
parameters["pdeath_vent_hc"]<-parameters["pdeath_vent_hc"]/100
parameters["pdeath_icu_hc"]<-parameters["pdeath_icu_hc"]/100
parameters["pdeath_icu_hco"]<-parameters["pdeath_icu_hco"]/100
parameters["reporth_g"]<-parameters["reporth_g"]/100
parameters["seroneg"]<-(1/parameters["seroneg"])
parameters_noise <- c("p", "rho", "omega", "gamma", "nui", "ihr_scaling","nus", "nu_icu","nu_vent",
"rhos", "selfis_eff", "dist_eff", "hand_eff", "mask_eff", "work_eff",
"w2h", "s2h", "cocoon_eff", "mean_imports", "screen_overdispersion",
"quarantine_effort", "quarantine_eff_home", "quarantine_eff_other")
# parameters_fit <- c("p", "ihr_scaling","ifr_correction_young","ifr_correction_old","init")
parameters_fit <- rownames(fit_mat)
###########################################################################
# Define the indices for each variable
Sindex<-1:A
Eindex<-(A+1):(2*A)
Iindex<-(2*A+1):(3*A)
Rindex<-(3*A+1):(4*A)
Xindex<-(4*A+1):(5*A)
Hindex<-(5*A+1):(6*A)
HCindex<-(6*A+1):(7*A)
Cindex<-(7*A+1):(8*A)
CMindex<-(8*A+1):(9*A)
Vindex<-(9*A+1):(10*A)
QSindex<-(10*A+1):(11*A)
QEindex<-(11*A+1):(12*A)
QIindex<-(12*A+1):(13*A)
QRindex<-(13*A+1):(14*A)
CLindex<-(14*A+1):(15*A)
QCindex<-(15*A+1):(16*A)
ICUindex<-(16*A+1):(17*A)
ICUCindex<-(17*A+1):(18*A)
ICUCVindex<-(18*A+1):(19*A)
Ventindex<-(19*A+1):(20*A)
VentCindex<-(20*A+1):(21*A)
CMCindex<-(21*A+1):(22*A)
Zindex<-(22*A+1):(23*A)
EVindex<-(23*A+1):(24*A)
ERindex<-(24*A+1):(25*A)
EVRindex<-(25*A+1):(26*A)
VRindex<-(26*A+1):(27*A)
QVindex<-(27*A+1):(28*A)
QEVindex<-(28*A+1):(29*A)
QEVRindex<-(29*A+1):(30*A)
QERindex<-(30*A+1):(31*A)
QVRindex<-(31*A+1):(32*A)
HCICUindex<-(32*A+1):(33*A)
HCVindex<-(33*A+1):(34*A)
Abindex<-(34*A+1):(35*A)
###########################################################################
# MODEL INITIAL CONDITIONS
initI<-0*popstruc[,2] # Infected and symptomatic
initE<-0*popstruc[,2] # Incubating
# initE[aci]<-1 # place random index case in E compartment
initE[aci]<-parameters["init"] # place random index case in E compartment
initR<-parameters["pre"]*popstruc[,2] # Immune
initX<-0*popstruc[,2] # Isolated
initV<-0*popstruc[,2] # Vaccinated
initQS<-0*popstruc[,2] # quarantined S
initQE<-0*popstruc[,2] # quarantined E
initQI<-0*popstruc[,2] # quarantined I
initQR<-0*popstruc[,2] # quarantined R
initH<-0*popstruc[,2] # hospitalised
initHC<-0*popstruc[,2] # hospital critical
initC<-0*popstruc[,2] # Cumulative cases (true)
initCM<-0*popstruc[,2] # Cumulative deaths (true)
initCL<-0*popstruc[,2] # symptomatic cases
initQC<-0*popstruc[,2] # quarantined C
initICU<-0*popstruc[,2] # icu
initICUC<-0*popstruc[,2] # icu critical
initICUCV<-0*popstruc[,2] # icu critical
initVent<-0*popstruc[,2] # icu vent
initVentC<-0*popstruc[,2] # icu vent crit
initCMC<-0*popstruc[,2] # Cumulative deaths - overload (true)
initZ<-0*popstruc[,2] # testing - quarantined (true)
initEV<-0*popstruc[,2] # vaccinated exposed
initER<-0*popstruc[,2] # recovered exposed
initEVR<-0*popstruc[,2] # recovered and vaccinated exposed
initVR<-0*popstruc[,2] # recovered and vaccinated
initQV<-0*popstruc[,2] # quarantined and vaccinated
initQEV<-0*popstruc[,2] # quarantined, exposed and vaccinated
initQEVR<-0*popstruc[,2] # quarantined, exposed, recovered and vaccinated
initQER<-0*popstruc[,2] # quarantined, exposed and recovered
initQVR<-0*popstruc[,2] # quarantined, recovered and vaccinated
initHCICU<-0*popstruc[,2] # icu not seeking
initHCV<-0*popstruc[,2] # ventilator not seeking
initAb<-0*popstruc[,2] # ventilator not seeking
initS<-popstruc[,2]-initE-initI-initCL-initR-initX-initZ-initV-initH-initHC-initICU-initICUC-initICUCV-initVent-initVentC-
initQS-initQE-initQI-initQR-initQC-initEV-initER-initEVR-initVR-initQV-initQEV-initQEVR-initQER-initQVR-
initHCICU-initHCV # Susceptible (non-immune)
inp <- read_excel(file_path, sheet = "Interventions") %>%
filter(! is.na(Intervention))
# Test if listed interventions are valid
valid_interventions_v17 <- c("Dexamethasone", "Handwashing", "International Travel Ban",
"Mask Wearing", "Mass Testing", "School Closures", "Self-isolation if Symptomatic",
"(*Self-isolation) Household Isolation", "(*Self-isolation) Screening", "Shielding the Elderly",
"Social Distancing", "Vaccination", "Working at Home")
if(all(inp$Intervention %in% valid_interventions_v17)) message("Okay, all interventions are valid.")
if(!all(inp$Intervention %in% valid_interventions_v17)) stop("Stop, some interventions are not valid.")
# complte the age_groups column
inp$`Age Groups`[is.na(inp$`Age Groups`)] <- "1-21"
vec<- inp$`Age Groups`
parse_age_group <- function(vec) {
regx_str <- "^(([0]*([1-9]|1[0-9]|2[0-1]))[\\,\\:])*([0]*([1-9]|1[0-9]|2[0-1]))$"
output <- rep(FALSE, 21)
vec <- str_replace_all(vec, "-", ":")
vec <- str_replace_all(vec, ";", ",")
vec <- str_replace_all(vec, "[:space:]", "")
vec <- str_replace_all(vec, "[:alpha:]", "")
if (!str_detect(vec, regx_str)) {
return(output)
}
vec <- paste0("c(", vec, ")")
vec2 <- eval(parse(text = vec))
output[vec2] <- TRUE
return(output)
}
inp$Target<-rep(0,length(inp$Value))
age_group_vectors <- list()
for (i in 1:length(vec)){
pp<-parse_age_group(vec[i])
age_group_vectors[[i]] <- pp
inp$Target[i]<-i
}
inputs<-function(inp, run){
# cap intervention start and end dates with simulation end date
inp$`Date Start` = pmin(stopdate, inp$`Date Start`)
inp$`Date End` = pmin(stopdate, inp$`Date End`)
inp <- inp %>% arrange(`Date Start`)
# print(inp)
tv<-which(inp$`Apply to`==run)
si<-intersect(which(inp$Intervention=="Self-isolation if Symptomatic"),tv)
scr<-intersect(which(inp$Intervention=="Screening (when S.I.)"),tv)
sd<-intersect(which(inp$Intervention=="Social Distancing"),tv)
hw<-intersect(which(inp$Intervention=="Handwashing"),tv)
msk<-intersect(which(inp$Intervention=="Masking"),tv)
wah<-intersect(which(inp$Intervention=="Working at Home"),tv)
sc<-intersect(which(inp$Intervention=="School Closures"),tv)
# scc<-intersect(which(inp$Intervention=="School Group Code"),tv)
cte<-intersect(which(inp$Intervention=="Shielding the Elderly"),tv)
q<-intersect(which(inp$Intervention=="Household Isolation (when S.I.)"),tv)
tb<-intersect(which(inp$Intervention=="International Travel Ban"),tv)
vc<-intersect(which(inp$Intervention=="Vaccination"),tv)
mt<-intersect(which(inp$Intervention=="Mass Testing"),tv)
vc<-intersect(which(inp$Intervention=="Vaccination"),tv)
dx<-intersect(which(inp$Intervention=="Dexamethasone"),tv)
v<-(format(as.POSIXct(inp$`Date Start`,format='%Y/%m/%d %H:%M:%S'),format="%d/%m/%y"))
v2<-as.Date(v,format="%d/%m/%y")
inp$`Date Start`<-v2
v<-(format(as.POSIXct(inp$`Date End`,format='%Y/%m/%d %H:%M:%S'),format="%d/%m/%y"))
v2<-as.Date(v,format="%d/%m/%y")
inp$`Date End`<-v2
## self isolation
f<-c()
si_vector<-c()
isolation<-c()
if (length(si)>=1){
for (i in 1:length(si)){
f<-c(f,as.numeric(inp$`Date Start`[si[i]]-startdate),as.numeric(inp$`Date End`[si[i]]-startdate))
# print(f)
if(i==1){
if (inp$`Date Start`[si[i]]>startdate){
si_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[si[i]],(f[i+1]-f[i])*20))
isolation<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
si_vector<-c(rep(inp$`Value`[si[i]],(f[i+1])*20))
isolation<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
si_vector<-c(si_vector,rep(inp$`Value`[si[i]],20))
isolation<-c(isolation,rep(1,20))
}else{
si_vector<-c(si_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
isolation<-c(isolation,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
si_vector<-c(si_vector,rep(inp$`Value`[si[i]],(f[i*2]-f[i*2-1])*20))
isolation<-c(isolation,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(si) && f[i*2]<tail(times,1)){
si_vector<-c(si_vector,rep(0,(tail(times,1)-f[i*2])*20))
isolation<-c(isolation,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
si_vector<-rep(0,tail(times,1)*20)
isolation<-rep(0,tail(times,1)*20)
}
## social distancing
f<-c()
sd_vector<-c()
distancing<-c()
if (length(sd)>=1){
for (i in 1:length(sd)){
f<-c(f,as.numeric(inp$`Date Start`[sd[i]]-startdate),as.numeric(inp$`Date End`[sd[i]]-startdate))
if(i==1){
if (inp$`Date Start`[sd[i]]>startdate){
sd_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[sd[i]],(f[i+1]-f[i])*20))
distancing<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
sd_vector<-c(rep(inp$`Value`[sd[i]],(f[i+1])*20))
distancing<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
sd_vector<-c(sd_vector,rep(inp$`Value`[sd[i]],20))
distancing<-c(distancing,rep(1,20))
}else{
sd_vector<-c(sd_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
distancing<-c(distancing,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
sd_vector<-c(sd_vector,rep(inp$`Value`[sd[i]],(f[i*2]-f[i*2-1])*20))
distancing<-c(distancing,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(sd)&& f[i*2]<tail(times,1)){
sd_vector<-c(sd_vector,rep(0,(tail(times,1)-f[i*2])*20))
distancing<-c(distancing,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
sd_vector<-rep(0,tail(times,1)*20)
distancing<-rep(0,tail(times,1)*20)
}
## screening
f<-c()
scr_vector<-c()
screen<-c()
if (length(scr)>=1){
for (i in 1:length(scr)){
f<-c(f,as.numeric(inp$`Date Start`[scr[i]]-startdate),as.numeric(inp$`Date End`[scr[i]]-startdate))
if(i==1){
if (inp$`Date Start`[scr[i]]>startdate){
scr_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[scr[i]],(f[i+1]-f[i])*20))
screen<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
scr_vector<-c(rep(inp$`Value`[scr[i]],(f[i+1])*20))
screen<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
scr_vector<-c(scr_vector,rep(inp$`Value`[scr[i]],20))
screen<-c(screen,rep(1,20))
}else{
scr_vector<-c(scr_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
screen<-c(screen,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
scr_vector<-c(scr_vector,rep(inp$`Value`[scr[i]],(f[i*2]-f[i*2-1])*20))
screen<-c(screen,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(scr)&& f[i*2]<tail(times,1)){
scr_vector<-c(scr_vector,rep(0,(tail(times,1)-f[i*2])*20))
screen<-c(screen,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
scr_vector<-rep(0,tail(times,1)*20)
screen<-rep(0,tail(times,1)*20)
}
## handwashing
f<-c()
hw_vector<-c()
handwash<-c()
if (length(hw)>=1){
for (i in 1:length(hw)){
f<-c(f,as.numeric(inp$`Date Start`[hw[i]]-startdate),as.numeric(inp$`Date End`[hw[i]]-startdate))
if(i==1){
if (inp$`Date Start`[hw[i]]>startdate){
hw_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[hw[i]],(f[i+1]-f[i])*20))
handwash<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
hw_vector<-c(rep(inp$`Value`[hw[i]],(f[i+1])*20))
handwash<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
hw_vector<-c(hw_vector,rep(inp$`Value`[hw[i]],20))
handwash<-c(handwash,rep(1,20))
}else{
hw_vector<-c(hw_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
handwash<-c(handwash,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
hw_vector<-c(hw_vector,rep(inp$`Value`[hw[i]],(f[i*2]-f[i*2-1])*20))
handwash<-c(handwash,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(hw)&& f[i*2]<tail(times,1)){
hw_vector<-c(hw_vector,rep(0,(tail(times,1)-f[i*2])*20))
handwash<-c(handwash,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
hw_vector<-rep(0,tail(times,1)*20)
handwash<-rep(0,tail(times,1)*20)
}
## masking
f<-c()
msk_vector<-c()
masking<-c()
if (length(msk)>=1){
for (i in 1:length(msk)){
f<-c(f,as.numeric(inp$`Date Start`[msk[i]]-startdate),as.numeric(inp$`Date End`[msk[i]]-startdate))
if(i==1){
if (inp$`Date Start`[msk[i]]>startdate){
msk_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[msk[i]],(f[i+1]-f[i])*20))
masking<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
msk_vector<-c(rep(inp$`Value`[msk[i]],(f[i+1])*20))
masking<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
msk_vector<-c(msk_vector,rep(inp$`Value`[msk[i]],20))
masking<-c(masking,rep(1,20))
}else{
msk_vector<-c(msk_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
masking<-c(masking,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
msk_vector<-c(msk_vector,rep(inp$`Value`[msk[i]],(f[i*2]-f[i*2-1])*20))
masking<-c(masking,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(msk)&& f[i*2]<tail(times,1)){
msk_vector<-c(msk_vector,rep(0,(tail(times,1)-f[i*2])*20))
masking<-c(masking,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
msk_vector<-rep(0,tail(times,1)*20)
masking<-rep(0,tail(times,1)*20)
}
## dexamethasone
f<-c()
dex<-c()
if (length(dx)>=1){
for (i in 1:length(dx)){
f<-c(f,as.numeric(inp$`Date Start`[dx[i]]-startdate),as.numeric(inp$`Date End`[dx[i]]-startdate))
if(i==1){
if (inp$`Date Start`[dx[i]]>startdate){
dex<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
dex<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
dex<-c(dex,rep(1,20))
}else{
dex<-c(dex,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
dex<-c(dex,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(dx)&& f[i*2]<tail(times,1)){
dex<-c(dex,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
dex<-rep(0,tail(times,1)*20)
}
## working at home
f<-c()
wah_vector<-c()
workhome<-c()
if (length(wah)>=1){
for (i in 1:length(wah)){
f<-c(f,as.numeric(inp$`Date Start`[wah[i]]-startdate),as.numeric(inp$`Date End`[wah[i]]-startdate))
if(i==1){
if (inp$`Date Start`[wah[i]]>startdate){
wah_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[wah[i]],(f[i+1]-f[i])*20))
workhome<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
wah_vector<-c(rep(inp$`Value`[wah[i]],(f[i+1])*20))
workhome<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
wah_vector<-c(wah_vector,rep(inp$`Value`[wah[i]],20))
workhome<-c(workhome,rep(1,20))
}else{
wah_vector<-c(wah_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
workhome<-c(workhome,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
wah_vector<-c(wah_vector,rep(inp$`Value`[wah[i]],(f[i*2]-f[i*2-1])*20))
workhome<-c(workhome,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(wah)&& f[i*2]<tail(times,1)){
wah_vector<-c(wah_vector,rep(0,(tail(times,1)-f[i*2])*20))
workhome<-c(workhome,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
wah_vector<-rep(0,tail(times,1)*20)
workhome<-rep(0,tail(times,1)*20)
}
## school closure
f<-c()
sc_vector<-c()
schoolclose<-c()
if (length(sc)>=1){
for (i in 1:length(sc)){
f<-c(f,as.numeric(inp$`Date Start`[sc[i]]-startdate),as.numeric(inp$`Date End`[sc[i]]-startdate))
if(i==1){
if (inp$`Date Start`[sc[i]]>startdate){
sc_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[sc[i]],(f[i+1]-f[i])*20))
schoolclose<-c(rep(0,f[i]*20),rep(inp$Target[sc[i]],(f[i+1]-f[i])*20))
}
else{
sc_vector<-c(rep(inp$`Value`[sc[i]],(f[i+1])*20))
schoolclose<-c(rep(inp$Target[sc[i]],(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
sc_vector<-c(sc_vector,rep(inp$`Value`[sc[i]],20))
schoolclose<-c(schoolclose,rep(inp$Target[sc[i]],20))
}else{
sc_vector<-c(sc_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
schoolclose<-c(schoolclose,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
sc_vector<-c(sc_vector,rep(inp$`Value`[sc[i]],(f[i*2]-f[i*2-1])*20))
schoolclose<-c(schoolclose,rep(inp$Target[sc[i]],(f[i*2]-f[i*2-1])*20))
}
if(i==length(sc)&& f[i*2]<tail(times,1)){
sc_vector<-c(sc_vector,rep(0,(tail(times,1)-f[i*2])*20))
schoolclose<-c(schoolclose,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
sc_vector<-rep(0,tail(times,1)*20)
schoolclose<-rep(0,tail(times,1)*20)
}
schoolclose[is.na(schoolclose)]<-1
## cocooning the elderly
f<-c()
cte_vector<-c()
cocoon<-c()
if (length(cte)>=1){
for (i in 1:length(cte)){
f<-c(f,as.numeric(inp$`Date Start`[cte[i]]-startdate),as.numeric(inp$`Date End`[cte[i]]-startdate))
if(i==1){
if (inp$`Date Start`[cte[i]]>startdate){
cte_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[cte[i]],(f[i+1]-f[i])*20))
cocoon<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
cte_vector<-c(rep(inp$`Value`[cte[i]],(f[i+1])*20))
cocoon<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
cte_vector<-c(cte_vector,rep(inp$`Value`[cte[i]],20))
cocoon<-c(cocoon,rep(1,20))
}else{
cte_vector<-c(cte_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
cocoon<-c(cocoon,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
cte_vector<-c(cte_vector,rep(inp$`Value`[cte[i]],(f[i*2]-f[i*2-1])*20))
cocoon<-c(cocoon,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(cte)&& f[i*2]<tail(times,1)){
cte_vector<-c(cte_vector,rep(0,(tail(times,1)-f[i*2])*20))
cocoon<-c(cocoon,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
cte_vector<-rep(0,tail(times,1)*20)
cocoon<-rep(0,tail(times,1)*20)
}
## quarantine
f<-c()
q_vector<-c()
quarantine<-c()
if (length(q)>=1){
for (i in 1:length(q)){
f<-c(f,as.numeric(inp$`Date Start`[q[i]]-startdate),as.numeric(inp$`Date End`[q[i]]-startdate))
if(i==1){
if (inp$`Date Start`[q[i]]>startdate){
q_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[q[i]],(f[i+1]-f[i])*20))
quarantine<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
q_vector<-c(rep(inp$`Value`[q[i]],(f[i+1])*20))
quarantine<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
q_vector<-c(q_vector,rep(inp$`Value`[q[i]],20))
quarantine<-c(quarantine,rep(1,20))
}else{
q_vector<-c(q_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
quarantine<-c(quarantine,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
q_vector<-c(q_vector,rep(inp$`Value`[q[i]],(f[i*2]-f[i*2-1])*20))
quarantine<-c(quarantine,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(q)&& f[i*2]<tail(times,1)){
q_vector<-c(q_vector,rep(0,(tail(times,1)-f[i*2])*20))
quarantine<-c(quarantine,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
q_vector<-rep(0,tail(times,1)*20)
quarantine<-rep(0,tail(times,1)*20)
}
## travel ban
f<-c()
tb_vector<-c()
travelban<-c()
if (length(tb)>=1){
for (i in 1:length(tb)){
f<-c(f,as.numeric(inp$`Date Start`[tb[i]]-startdate),as.numeric(inp$`Date End`[tb[i]]-startdate))
if(i==1){
if (inp$`Date Start`[tb[i]]>startdate){
tb_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[tb[i]],(f[i+1]-f[i])*20))
travelban<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
}
else{
tb_vector<-c(rep(inp$`Value`[tb[i]],(f[i+1])*20))
travelban<-c(rep(1,(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
tb_vector<-c(tb_vector,rep(inp$`Value`[tb[i]],20))
travelban<-c(travelban,rep(1,20))
}else{
tb_vector<-c(tb_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
travelban<-c(travelban,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
tb_vector<-c(tb_vector,rep(inp$`Value`[tb[i]],(f[i*2]-f[i*2-1])*20))
travelban<-c(travelban,rep(1,(f[i*2]-f[i*2-1])*20))
}
if(i==length(tb)&& f[i*2]<tail(times,1)){
tb_vector<-c(tb_vector,rep(0,(tail(times,1)-f[i*2])*20))
travelban<-c(travelban,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
tb_vector<-rep(0,tail(times,1)*20)
travelban<-rep(0,tail(times,1)*20)
}
## mass testing
f<-c()
mt_vector<-c()
masstesting<-c()
testage<-c()
if (length(mt)>=1){
for (i in 1:length(mt)){
f<-c(f,as.numeric(inp$`Date Start`[mt[i]]-startdate),as.numeric(inp$`Date End`[mt[i]]-startdate))
if(i==1){
if (inp$`Date Start`[mt[i]]>startdate){
mt_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[mt[i]],(f[i+1]-f[i])*20))
masstesting<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
testage<-c(rep(0,f[i]*20),rep(inp$`Target`[mt[i]],(f[i+1]-f[i])*20))
}
else{
mt_vector<-c(rep(inp$`Value`[mt[i]],(f[i+1])*20))
masstesting<-c(rep(1,(f[i+1])*20))
testage<-c(rep(inp$`Target`[mt[i]],(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
mt_vector<-c(mt_vector,rep(inp$`Value`[mt[i]],20))
masstesting<-c(masstesting,rep(1,20))
testage<-c(testage,rep(inp$`Target`[mt[i]],20))
}else{
mt_vector<-c(mt_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
masstesting<-c(masstesting,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
testage<-c(testage,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
mt_vector<-c(mt_vector,rep(inp$`Value`[mt[i]],(f[i*2]-f[i*2-1])*20))
masstesting<-c(masstesting,rep(1,(f[i*2]-f[i*2-1])*20))
testage<-c(testage,rep(inp$`Target`[mt[i]],(f[i*2]-f[i*2-1])*20))
}
if(i==length(mt)&& f[i*2]<tail(times,1)){
mt_vector<-c(mt_vector,rep(0,(tail(times,1)-f[i*2])*20))
masstesting<-c(masstesting,rep(0,(tail(times,1)-f[i*2])*20))
testage<-c(testage,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
mt_vector<-rep(0,tail(times,1)*20)
masstesting<-rep(0,tail(times,1)*20)
testage<-rep(0,tail(times,1)*20)
}
## vaccine
f<-c()
vc_vector<-c()
vaccine<-c()
vaccineage<-c()
if (length(vc)>=1){
for (i in 1:length(vc)){
f<-c(f,as.numeric(inp$`Date Start`[vc[i]]-startdate),as.numeric(inp$`Date End`[vc[i]]-startdate))
if(i==1){
if (inp$`Date Start`[vc[i]]>startdate){
vc_vector<-c(rep(0,f[i]*20),rep(inp$`Value`[vc[i]],(f[i+1]-f[i])*20))
vaccine<-c(rep(0,f[i]*20),rep(1,(f[i+1]-f[i])*20))
vaccineage<-c(rep(0,f[i]*20),rep(inp$`Target`[vc[i]],(f[i+1]-f[i])*20))
}
else{
vc_vector<-c(rep(inp$`Value`[vc[i]],(f[i+1])*20))
vaccine<-c(rep(1,(f[i+1])*20))
vaccineage<-c(rep(inp$`Target`[vc[i]],(f[i+1])*20))
}
}
else{
if (f[(i-1)*2+1]-f[(i-1)*2]==1){
vc_vector<-c(vc_vector,rep(inp$`Value`[vc[i]],20))
vaccine<-c(vaccine,rep(1,20))
vaccineage<-c(vaccineage,rep(inp$`Target`[vc[i]],20))
}else{
vc_vector<-c(vc_vector,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
vaccine<-c(vaccine,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
vaccineage<-c(vaccineage,rep(0,(f[(i-1)*2+1]-f[(i-1)*2])*20))
}
vc_vector<-c(vc_vector,rep(inp$`Value`[vc[i]],(f[i*2]-f[i*2-1])*20))
vaccine<-c(vaccine,rep(1,(f[i*2]-f[i*2-1])*20))
vaccineage<-c(vaccineage,rep(inp$`Target`[vc[i]],(f[i*2]-f[i*2-1])*20))
}
if(i==length(vc)&& f[i*2]<tail(times,1)){
vc_vector<-c(vc_vector,rep(0,(tail(times,1)-f[i*2])*20))
vaccine<-c(vaccine,rep(0,(tail(times,1)-f[i*2])*20))
vaccineage<-c(vaccineage,rep(0,(tail(times,1)-f[i*2])*20))
}
}
}else{
vc_vector<-rep(0,tail(times,1)*20)
vaccine<-rep(0,tail(times,1)*20)
vaccineage<-rep(0,tail(times,1)*20)
}
return(list(si_vector=si_vector,sd_vector=sd_vector,scr_vector=scr_vector,hw_vector=hw_vector,msk_vector=msk_vector,
wah_vector=wah_vector,sc_vector=sc_vector,tb_vector=tb_vector,mt_vector=mt_vector*1000,
cte_vector=cte_vector,q_vector=q_vector,vc_vector=vc_vector,isolation=isolation,
screen=screen,cocoon=cocoon,schoolclose=schoolclose,workhome=workhome,handwash=handwash,masking=masking,
quarantine=quarantine,vaccine=vaccine,travelban=travelban,distancing=distancing,masstesting=masstesting,
testage=testage,vaccineage=vaccineage,dex=dex))
}
vectors0<-inputs(inp,'Baseline (Calibration)')
vectors<-inputs(inp,'Hypothetical Scenario')
f <- c(1,(1+parameters["give"])/2,(1-parameters["give"])/2,0)
KH<-parameters["beds_available"]
KICU<- parameters["icu_beds_available"]+parameters["ventilators_available"]
Kvent<- parameters["ventilators_available"]
x.H <- c(0,(1+parameters["give"])*KH/2,(3-parameters["give"])*KH/2,2*KH)
x.ICU <- c(0,(1+parameters["give"])*KICU/2,(3-parameters["give"])*KICU/2,2*KICU)
x.Vent <- c(0,(1+parameters["give"])*Kvent/2,(3-parameters["give"])*Kvent/2,2*Kvent)
fH <- splinefun(x.H, f, method = "hyman")
fICU <- splinefun(x.ICU, f, method = "hyman")
fVent<- splinefun(x.Vent, f, method = "hyman")
# set up a function to solve the equations
covid<-function(t, Y, parameters,input)
{
with(as.list(c(Y, parameters)),
{
S <- Y[Sindex]
E <- Y[Eindex]
I <- Y[Iindex]
R <- Y[Rindex]
X <- Y[Xindex]
Z <- Y[Zindex]
H <- Y[Hindex]
HC <- Y[HCindex]
C <- Y[Cindex]
CM <- Y[CMindex]
V <- Y[Vindex]
QS <- Y[QSindex]
QE <- Y[QEindex]
QI <- Y[QIindex]
QR <- Y[QRindex]
CL <- Y[CLindex]
QC <- Y[QCindex]
ICU <- Y[ICUindex]
ICUC <- Y[ICUCindex]
ICUCV <-Y[ICUCVindex]
Vent <- Y[Ventindex]
VentC <- Y[VentCindex]
CMC <- Y[CMCindex]
EV <- Y[EVindex]
ER <- Y[ERindex]
EVR <- Y[EVRindex]
VR <- Y[VRindex]
QV <- Y[QVindex]
QEV <- Y[QEVindex]
QER <- Y[QERindex]
QEVR <- Y[QEVRindex]
QVR <- Y[QVRindex]
HCICU <- Y[HCICUindex]
HCV <- Y[HCVindex]
Ab <- Y[Abindex]
P <- (S+E+I+R+X+Z+V+H+HC+ICU+ICUC+ICUCV+Vent+VentC+EV+ER+EVR+VR+HCICU+HCV+
QS+QE+QI+QR+CL+QC+QEV+QV+QER+QEVR+QVR)
Q <- (sum(QS)+sum(QE)+sum(QI)+sum(QC)+sum(QR)+sum(QV)+sum(QER)+sum(QEVR)+sum(QEV)+sum(QVR))/sum(P)
# print(HCICUindex)
# print(sum(ER))
# print(t)
# print(paste("EVR",sum(QE)))
# print(paste("ER",sum(QR)))
# print(paste("QER",sum(QER)))
# print(paste("QEVR",sum(QEVR)))
# print(paste("QV",sum(QV)))
# print(paste("QC",sum(QC)))
# print(paste("QI",sum(QI)))
# print(paste("QEV",sum(QV)))
# print(paste("QE",sum(QC)))
# print(paste("QR",sum(QR)))
# health system performance
critH<-min(1-fH(sum(H)+sum(ICUC)+sum(ICUCV)),1)
crit<-min(1-fICU(sum(ICU)+sum(Vent)+sum(VentC)),1)
critV<-min(1-fVent(sum(Vent)),1)
# interventions
isolation<-input$isolation[t*20+1]
distancing<-input$distancing[t*20+1]
handwash<-input$handwash[t*20+1]
masking<-input$masking[t*20+1]
workhome<-input$workhome[t*20+1]
schoolclose<-input$schoolclose[t*20+1]
cocoon<-input$cocoon[t*20+1]
vaccine<-input$vaccine[t*20+1]
travelban<-input$travelban[t*20+1]
screen<-input$screen[t*20+1]
quarantine<-input$quarantine[t*20+1]
masstesting<-input$masstesting[t*20+1]
dexamethasone<-input$dex[t*20+1]
screen_eff<-0
selfis<-0
school<-1
dist<-1
hand<-0
mask<-0
vaccinate<-0
trvban_eff<-0
quarantine_rate<-0
tests_per_day<-0
selfis_cov<-(input$si_vector[t*20+1])/100
screen_contacts<-(input$scr_vector[t*20+1])/10
school_eff<-(input$sc_vector[t*20+1])/100
dist_cov<-(input$sd_vector[t*20+1])/100
hand_cov<-(input$hw_vector[t*20+1])/100
mask_cov<-(input$msk_vector[t*20+1])/100
cocoon<-(input$cte_vector[t*20+1])/100
work_cov<-(input$wah_vector[t*20+1])/100
travelban_eff<-(input$tb_vector[t*20+1])/100
vaccine_cov<-(input$vc_vector[t*20+1])/100
quarantine_cov<-(input$q_vector[t*20+1])/100
tests_per_day<-(input$mt_vector[t*20+1])
vaccineage<-input$vaccineage[t*20+1]
testage<-input$testage[t*20+1]
if (vaccine){
age_vaccine_vector<-as.numeric(age_group_vectors[[vaccineage]])
vac_rate<-(-log(1-vaccine_cov)/vac_campaign)
vaccinate<-vac_rate
}else{age_vaccine_vector<-rep(0,A)}
# print(vaccinate*age_vaccine_vector)
if (masstesting){
age_testing_vector<-as.numeric(age_group_vectors[[testage]])
}else{age_testing_vector<-rep(0,A)}
if (workhome){
work<-work_cov*work_eff
}else{work<-1}
if (isolation){
selfis<-selfis_cov
if(screen){
screen_eff<-min(sum(report*I+reportc*(CL)+H+ICU+Vent+reporth*(HC+ICUC+ICUCV+VentC+HCICU+HCV))*screen_contacts*(screen_overdispersion*I/P)*screen_test_sens/P,1)
}
}
if (schoolclose>=1){
school<-school_eff
schoolclose2<-as.numeric(age_group_vectors[[schoolclose]])
}else{schoolclose2<-0}
# print(schoolclose2)
# print(schoolclose)
if(distancing){
dist<-dist_cov*dist_eff
}
if(handwash){
hand<-hand_eff*hand_cov
}
if(masking){
mask<-mask_eff*mask_cov
}
if(travelban){
trvban_eff<-travelban_eff
}
if(quarantine){
rate_q<-min((min(sum((CL+H+ICU+Vent+HC+ICUC+ICUCV+VentC+HCV+HCICU))*(household_size-1)/sum(P),1)*quarantine_effort),quarantine_cov/2)
quarantine_rate<-rate_q/(1+exp(-10*(quarantine_cov/2-Q)))
}
if(dexamethasone){
prob_v<-prob_vent*vent_dex
}else{
dexo2<-1;dexo2c<-1;dexv<-1;dexvc<-1;prob_v<-prob_vent;
}
# print(paste(dexo2,dexo2c,dexv,dexvc,prob_v))
# print(paste("quarantine_rate",quarantine_rate))
# print(quarantine_rate*sum(R))
# print(quarantine_rate*ER)
#
# print(sum(vaccinate*age_vaccine_vector*R))
# print((1/quarantine_days)*sum(QR))
# testing rates
propI<-sum(I)/sum(P)
propC<-sum(CL)/sum(P)
propE<-sum(E)/sum(P)
propEV<-sum(EV)/sum(P)
propER<-sum(ER)/sum(P)
propEVR<-sum(EVR)/sum(P)
propHC<-sum(HC)/sum(P)
propHCICU<-sum(HCICU)/sum(P)
propHCV<-sum(HCV)/sum(P)
testE<-tests_per_day*propE
testEV<-tests_per_day*propEV
testER<-tests_per_day*propER
testEVR<-tests_per_day*propEVR
testI<-tests_per_day*propI
testC<-tests_per_day*propC
testHC<-tests_per_day*propHC
testHCICU<-tests_per_day*propHCICU
testHCV<-tests_per_day*propHCV
if(sum(I)>1){
ratetestI<-mass_test_sens*testI/sum(I)
# print(paste('rateI: ',ratetestI))
}else{ratetestI<-0}
if(sum(CL)>1){
ratetestC<-mass_test_sens*testC/sum(CL)
# print(paste('rateC: ',ratetestC))
}else{ratetestC<-0}
# print(sum(E))
if(sum(E)>1){
ratetestE<-mass_test_sens*testE/sum(E)
}else{ratetestE<-0}
if(sum(EV)>1){
ratetestEV<-mass_test_sens*testEV/sum(EV)
# print(paste('rateEV: ',ratetestEV))
}else{ratetestEV<-0}
if(sum(ER)>1){
ratetestER<-mass_test_sens*testER/sum(ER)
# print(paste('rateER: ',ratetestER))
}else{ratetestER<-0}
if(sum(EVR)>1){
ratetestEVR<-mass_test_sens*testEVR/sum(EVR)
}else{ratetestEVR<-0}
if(sum(HC)>1){
ratetestHC<-mass_test_sens*testHC/sum(HC)
}else{ratetestHC<-0}
if(sum(HCICU)>1){
ratetestHCICU<-mass_test_sens*testHCICU/sum(HCICU)
}else{ratetestHCICU<-0}
if(sum(HCV)>1){
ratetestHCV<-mass_test_sens*testHCV/sum(HCV)
}else{ratetestHCV<-0}
# print(mass_test_sens)
# print(ratetestI*sum(I) + ratetestC*sum(CL) - (1/isolation_days)*sum(Z) )
# print(propC)
# print(testI)
# print(testC)
#
# cocooning the elderly
cocoon_mat<-matrix((1-cocoon_eff),nrow = length(popstruc$pop),ncol = length(popstruc$pop))
cocoon_mat[1:(age_cocoon-1),1:(age_cocoon-1)]<-1
# contact matrices
cts<-(contact_home+distancing*(1-dist)*contact_other+(1-distancing)*contact_other
+(1-schoolclose2)*contact_school # school on
+schoolclose2*(1-school)*contact_school # school close
+schoolclose2*contact_home*school*s2h # inflating contacts at home when school closes
+(1-workhome)*contact_work # normal work
+workhome*(1-work)*contact_work # people not working from home when homework is active
+contact_home*workhome*work*w2h # inflating contacts at home when working from home
)
# Final transmission related parameters
contacts <- (1-cocoon)*cts+cocoon*cts*cocoon_mat+cocoon*(1+schoolclose2*(1-school_eff)+workhome*(1-work_eff))*contact_home*(1-cocoon_mat)
seas <- 1+amp*cos(2*3.14*(t-(phi*365.25/12))/365.25)
importation <- mean_imports*(1-trvban_eff)
HH<-H+ICU+Vent+ICUC+ICUCV+VentC
HHC<-HC+HCICU+HCV
lam <- (1-max(hand,mask))*p*seas*(contacts%*%((rho*E+(I+CL+importation)+(1-selfis_eff)*(X+HHC)+rhos*(HH))/P))+
(1-max(hand,mask))*p*seas*(1-quarantine*quarantine_eff_other)*(contact_other%*%((rho*QE+QI+QC+QEV+QEVR+QER)/P))
# contacts under home quarantine
lamq<-(1-max(hand,mask))*p*seas*((1-quarantine_eff_home)*contact_home%*%(((1-selfis_eff)*(X+HHC+rho*QE+QI+QC++QEV+QEVR+QER))/P))+
(1-max(hand,mask))*p*seas*(1-quarantine_eff_other)*(contact_other%*%((rho*E+(I+CL+importation)+(1-selfis_eff)*(X+HHC+rho*QE+QI+QC++QEV+QEVR+QER)+rhos*(HH))/P))
# lamq<-0
# print(paste("lamq",lamq))
# print(paste("lamq",(1-vaccine_eff_r)*lamq*sum(QVR) ))
# print(paste("quarantine evr",quarantine_rate*sum(EVR) ))
# birth/death
b1<-sum(popbirth[,2]*popstruc[,2])
birth<-0*popbirth[,2]
birth[1]<-b1
# ODE system
dSdt <- -S*lam-vaccinate*age_vaccine_vector*S+omega*R+vac_dur*V-
quarantine_rate*S +(1/quarantine_days)*QS+ageing%*%S-mort*S+birth
dEdt <- S*lam-gamma*E+ageing%*%E- vaccinate*age_vaccine_vector*E - mort*E -
quarantine_rate*E+(1/quarantine_days)*QE
dIdt <- gamma*(1-pclin)*(1-age_testing_vector*ratetestE)*(1-screen_eff)*(1-ihr[,2])*E+
gamma*(1-pclin_v)*(1-age_testing_vector*ratetestEV)*(1-screen_eff)*(1-sigmaEV*ihr[,2])*EV+
gamma*(1-pclin_vr)*(1-age_testing_vector*ratetestEVR)*(1-screen_eff)*(1-sigmaEVR*ihr[,2])*EVR+
gamma*(1-pclin_r)*(1-age_testing_vector*ratetestER)*(1-screen_eff)*(1-sigmaER*ihr[,2])*ER-
vaccinate*age_vaccine_vector*I - nui*I+ageing%*%I-mort*I +
(1/quarantine_days)*QI - quarantine_rate*I - ratetestI*age_testing_vector*I
dCLdt<- gamma*pclin*(1-age_testing_vector*ratetestE)*(1-selfis)*(1-ihr[,2])*(1-quarantine_rate)*E+
gamma*pclin_v*(1-age_testing_vector*ratetestEV)*(1-selfis)*(1-sigmaEV*ihr[,2])*(1-quarantine_rate)*EV+
gamma*pclin_vr*(1-age_testing_vector*ratetestEVR)*(1-selfis)*(1-sigmaEVR*ihr[,2])*(1-quarantine_rate)*EVR+
gamma*pclin_r*(1-age_testing_vector*ratetestER)*(1-selfis)*(1-sigmaER*ihr[,2])*(1-quarantine_rate)*ER-
nui*CL+ageing%*%CL-mort*CL + (1/quarantine_days)*QC - ratetestC*age_testing_vector*CL
dRdt <- vac_dur_r*VR-omega*R-vaccinate*age_vaccine_vector*R-lam*sigmaR*R - quarantine_rate*R+
nui*I+nui*X+nui*CL+ageing%*%R-mort*R + (1/isolation_days)*Z+(1/quarantine_days)*QR+
nus*propo2*(1-dexo2*pdeath_ho)*ifr[,2]*H+nus*(1-propo2)*(1-pdeath_h)*ifr[,2]*H+
nusc*propo2*(1-pdeath_hco)*ifr[,2]*HC+nusc*(1-propo2)*(1-pdeath_hc)*ifr[,2]*HC+
nusc*propo2*(1-pdeath_icu_hco)*ifr[,2]*HCICU+nusc*(1-propo2)*(1-pdeath_icu_hc)*ifr[,2]*HCICU+
nu_icu*propo2*(1-dexo2*pdeath_icuo)*ifr[,2]*ICU+nu_icu*(1-propo2)*(1-pdeath_icu)*ifr[,2]*ICU+
nu_icuc*propo2*(1-dexo2c*pdeath_icuco)*ifr[,2]*ICUC+nu_icuc*(1-propo2)*(1-pdeath_icuc)*ifr[,2]*ICUC+
nu_vent*(1-dexv*pdeath_vent)*ifr[,2]*Vent+
nu_ventc*(1-pdeath_vent_hc)*ifr[,2]*HCV+
nu_ventc*(1-dexvc*pdeath_ventc)*ifr[,2]*VentC+nu_ventc*(1-dexvc*pdeath_ventc)*ifr[,2]*ICUCV
dXdt <- gamma*selfis*(1-age_testing_vector*ratetestE)*pclin*(1-ihr[,2])*E+
gamma*(1-pclin)*(1-age_testing_vector*ratetestE)*screen_eff*(1-ihr[,2])*E+
gamma*selfis*(1-age_testing_vector*ratetestEV)*pclin_v*(1-sigmaEV*ihr[,2])*EV+
gamma*(1-pclin_v)*(1-age_testing_vector*ratetestEV)*screen_eff*(1-sigmaEV*ihr[,2])*EV+
gamma*selfis*(1-age_testing_vector*ratetestEVR)*pclin_v*(1-sigmaEVR*ihr[,2])*EVR+
gamma*(1-pclin_vr)*(1-age_testing_vector*ratetestEVR)*screen_eff*(1-sigmaEVR*ihr[,2])*EVR+
gamma*selfis*(1-age_testing_vector*ratetestER)*pclin_r*(1-sigmaER*ihr[,2])*ER+
gamma*(1-pclin_r)*(1-age_testing_vector*ratetestER)*screen_eff*(1-sigmaER*ihr[,2])*ER+
-nui*X+ageing%*%X-mort*X
dVdt <- vaccinate*age_vaccine_vector*S + omega*VR - (1-vaccine_eff)*lam*V - vac_dur*V + ageing%*%V-mort*V - quarantine_rate*V
dEVdt<- (1-vaccine_eff)*lam*V - gamma*EV + ageing%*%EV - mort*EV - quarantine_rate*EV +(1/quarantine_days)*QEV
dERdt<- lam*sigmaR*R - gamma*ER + ageing%*%ER - mort*ER - quarantine_rate*ER +(1/quarantine_days)*QER
dVRdt <- vaccinate*age_vaccine_vector*E + vaccinate*age_vaccine_vector*I + vaccinate*age_vaccine_vector*R -
(1-vaccine_eff_r)*lam*VR - vac_dur_r*VR + ageing%*%VR - mort*VR - omega*VR - quarantine_rate*VR + (1/quarantine_days)*QVR
dEVRdt<- (1-vaccine_eff_r)*lam*VR - gamma*EVR + ageing%*%EVR-mort*EVR - quarantine_rate*EVR +
(1/quarantine_days)*QEVR
dQSdt <- quarantine_rate*S + ageing%*%QS-mort*QS - (1/quarantine_days)*QS - lamq*QS
dQEdt <- quarantine_rate*E - gamma*QE + ageing%*%QE-mort*QE - (1/quarantine_days)*QE + lamq*QS
dQIdt <- quarantine_rate*I + gamma*(1-ihr[,2])*(1-pclin)*QE+
gamma*(1-sigmaEV*ihr[,2])*(1-pclin_v)*QEV+
gamma*(1-sigmaER*ihr[,2])*(1-pclin_r)*QER+
gamma*(1-sigmaEVR*ihr[,2])*(1-pclin_vr)*QEVR-
nui*QI+ageing%*%QI-mort*QI - (1/quarantine_days)*QI
dQCdt <- gamma*pclin*(1-selfis)*(1-age_testing_vector*ratetestE)*(1-ihr[,2])*quarantine_rate*E+
gamma*pclin_v*(1-age_testing_vector*ratetestEV)*(1-selfis)*(1-sigmaEV*ihr[,2])*quarantine_rate*EV+
gamma*pclin_vr*(1-age_testing_vector*ratetestEVR)*(1-selfis)*(1-sigmaEVR*ihr[,2])*quarantine_rate*EVR+
gamma*pclin_r*(1-age_testing_vector*ratetestER)*(1-selfis)*(1-sigmaER*ihr[,2])*quarantine_rate*ER+
gamma*(1-ihr[,2])*pclin*QE +
gamma*(1-sigmaEV*ihr[,2])*pclin_v*QEV +
gamma*(1-sigmaER*ihr[,2])*pclin_r*QER +
gamma*(1-sigmaEVR*ihr[,2])*pclin_vr*QEVR -
nui*QC+ageing%*%QC-mort*QC - (1/quarantine_days)*QC
dQRdt <- quarantine_rate*R + nui*QI + nui*QC + ageing%*%QR-mort*QR - (1/quarantine_days)*QR + vac_dur_r*QVR
dQVdt <- quarantine_rate*V + ageing%*%QV-mort*QV - (1/quarantine_days)*QV - (1-vaccine_eff)*lamq*QV + omega*QVR
dQEVdt <- quarantine_rate*EV - gamma*QEV + ageing%*%QEV-mort*QEV - (1/quarantine_days)*QEV + (1-vaccine_eff)*lamq*QV
dQERdt <- quarantine_rate*ER - gamma*QER + ageing%*%QER-mort*QER - (1/quarantine_days)*QER + sigmaR*lamq*QR
dQVRdt <- quarantine_rate*VR - (1-vaccine_eff_r)*lam*QVR - vac_dur_r*QVR - omega*QVR + ageing%*%QVR - mort*QVR
dQEVRdt <- quarantine_rate*EVR - gamma*QEVR +ageing%*%QEVR-mort*QEVR -
(1/quarantine_days)*QEVR +(1-vaccine_eff_r)*lamq*QVR
dHdt <- gamma*ihr[,2]*(1-prob_icu)*(1-critH)*reporth*E+
gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*(1-critH)*reporth*EV +
gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*(1-critH)*reporth*EVR +
gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*(1-critH)*reporth*ER +
gamma*ihr[,2]*(1-prob_icu)*(1-critH)*reporth*QE +
gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*(1-critH)*reporth*QEV +
gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*(1-critH)*reporth*QEVR +
gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*(1-critH)*reporth*QER -
nus*H + ageing%*%H-mort*H
dHCdt <- gamma*ihr[,2]*(1-prob_icu)*(1-reporth)*E+gamma*ihr[,2]*(1-prob_icu)*critH*reporth*E +
gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*(1-reporth)*EV+gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*critH*reporth*EV+
gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*(1-reporth)*EVR+gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*critH*reporth*EVR+
gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*(1-reporth)*ER+gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*critH*reporth*ER +
gamma*ihr[,2]*(1-prob_icu)*(1-reporth)*QE+gamma*ihr[,2]*(1-prob_icu)*critH*reporth*QE+
gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*(1-reporth)*QEV+gamma*sigmaEV*ihr[,2]*(1-prob_icu_v)*critH*reporth*QEV+
gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*(1-reporth)*QEVR+gamma*sigmaEVR*ihr[,2]*(1-prob_icu_vr)*critH*reporth*QEVR+
gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*(1-reporth)*QER+gamma*sigmaER*ihr[,2]*(1-prob_icu_r)*critH*reporth*QER -
nusc*HC + ageing%*%HC-mort*HC - ratetestHC*age_testing_vector*HC
dHCICUdt <- gamma*(1-reporth_ICU)*ihr[,2]*prob_icu*(1-prob_v)*E+
gamma*(1-reporth_ICU)*sigmaEV*ihr[,2]*prob_icu_v*(1-prob_v_v)*EV+
gamma*(1-reporth_ICU)*sigmaEVR*ihr[,2]*prob_icu_vr*(1-prob_v_vr)*EVR+
gamma*(1-reporth_ICU)*sigmaER*ihr[,2]*prob_icu_r*(1-prob_v_r)*ER+
gamma*(1-reporth_ICU)*ihr[,2]*prob_icu*(1-prob_v)*QE+
gamma*(1-reporth_ICU)*sigmaEV*ihr[,2]*prob_icu_v*(1-prob_v_v)*QEV+
gamma*(1-reporth_ICU)*sigmaEVR*ihr[,2]*prob_icu_vr*(1-prob_v_vr)*QEVR+
gamma*(1-reporth_ICU)*sigmaER*ihr[,2]*prob_icu_r*(1-prob_v_r)*QER-
nusc*HCICU + ageing%*%HCICU-mort*HCICU - ratetestHCICU*age_testing_vector*HCICU
dHCVdt <- gamma*(1-reporth_ICU)*ihr[,2]*prob_icu*prob_v*E+
gamma*(1-reporth_ICU)*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*EV+
gamma*(1-reporth_ICU)*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*EVR+
gamma*(1-reporth_ICU)*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*ER+
gamma*(1-reporth_ICU)*ihr[,2]*prob_icu*prob_v*QE+
gamma*(1-reporth_ICU)*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*QEV+
gamma*(1-reporth_ICU)*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*QEVR+
gamma*(1-reporth_ICU)*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*QER-
nu_ventc*HCV + ageing%*%HCV-mort*HCV - ratetestHCV*age_testing_vector*HCV
dICUdt <- gamma*reporth_ICU*ihr[,2]*prob_icu*(1-crit)*(1-prob_v)*E+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*(1-crit)*(1-prob_v_v)*EV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*(1-crit)*(1-prob_v_vr)*EVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*(1-crit)*(1-prob_v_r)*ER+
gamma*reporth_ICU*ihr[,2]*prob_icu*(1-crit)*(1-prob_v)*QE+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*(1-crit)*(1-prob_v_v)*QEV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*(1-crit)*(1-prob_v_vr)*QEVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*(1-crit)*(1-prob_v_r)*QER -
nu_icu*ICU +ageing%*%ICU - mort*ICU + (1-crit)*ICUC*1/2
dICUCdt <- gamma*reporth_ICU*ihr[,2]*prob_icu*crit*(1-prob_v)*E+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*crit*(1-prob_v_v)*EV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*crit*(1-prob_v_vr)*EVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*crit*(1-prob_v_r)*ER+
gamma*reporth_ICU*ihr[,2]*prob_icu*crit*(1-prob_v)*QE+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*crit*(1-prob_v_v)*QEV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*crit*(1-prob_v_vr)*QEVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*crit*(1-prob_v_r)*QER -
nu_icuc*ICUC -(1-crit)*ICUC*1/2 +ageing%*%ICUC - mort*ICUC
dICUCVdt <- gamma*reporth_ICU*ihr[,2]*prob_icu*prob_v*crit*E+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*crit*EV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*crit*EVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*crit*ER+
gamma*reporth_ICU*ihr[,2]*prob_icu*prob_v*crit*QE+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*crit*QEV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*crit*QEVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*crit*QER -
nu_ventc*ICUCV +ageing%*%ICUCV - mort*ICUCV - (1-critV)*ICUCV*1/2
dVentdt <- gamma*reporth_ICU*ihr[,2]*prob_icu*(1-crit)*(1-critV)*prob_v*E+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*(1-crit)*(1-critV)*prob_v_v*EV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*(1-crit)*(1-critV)*prob_v_vr*EVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*(1-crit)*(1-critV)*prob_v_r*ER+
gamma*reporth_ICU*ihr[,2]*prob_icu*(1-crit)*(1-critV)*prob_v*QE+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*(1-crit)*(1-critV)*prob_v_v*QEV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*(1-crit)*(1-critV)*prob_v_vr*QEVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*(1-crit)*(1-critV)*prob_v_r*QER +
(1-critV)*VentC*1/2 +(1-critV)*ICUCV*1/2 - nu_vent*Vent +ageing%*%Vent - mort*Vent
dVentCdt <- gamma*reporth_ICU*ihr[,2]*prob_icu*prob_v*(1-crit)*critV*E+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*(1-crit)*critV*EV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*(1-crit)*critV*EVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*(1-crit)*critV*ER+
gamma*reporth_ICU*ihr[,2]*prob_icu*prob_v*(1-crit)*critV*QE+
gamma*reporth_ICU*sigmaEV*ihr[,2]*prob_icu_v*prob_v_v*(1-crit)*critV*QEV+
gamma*reporth_ICU*sigmaEVR*ihr[,2]*prob_icu_vr*prob_v_vr*(1-crit)*critV*QEVR+
gamma*reporth_ICU*sigmaER*ihr[,2]*prob_icu_r*prob_v_r*(1-crit)*critV*QER -
(1-critV)*VentC*1/2 -nu_ventc*VentC +ageing%*%VentC - mort*VentC
# print(dexo2)
dCdt <- report*gamma*(1-age_testing_vector*ratetestE)*(1-pclin)*(1-ihr[,2])*(E+QE)+reportc*gamma*pclin*(1-age_testing_vector*ratetestE)*(1-ihr[,2])*(E+QE)+
gamma*ihr[,2]*(1-critH)*(1-prob_icu)*(E+QE)+gamma*ihr[,2]*critH*reporth*(1-prob_icu)*(E+QE)+
gamma*ihr[,2]*prob_icu*(E+QE)+ratetestI*age_testing_vector*I+ratetestC*age_testing_vector*CL+gamma*age_testing_vector*ratetestE*(1-ihr[,2])*E
dCMdt<- nus*propo2*dexo2*pdeath_ho*ifr[,2]*H+nus*(1-propo2)*pdeath_h*ifr[,2]*H+
nusc*report_death_HC*propo2*pdeath_hco*ifr[,2]*HC+nusc*report_death_HC*(1-propo2)*pdeath_hc*ifr[,2]*HC+
nu_icu*propo2*dexo2*pdeath_icuo*ifr[,2]*ICU+nu_icu*(1-propo2)*pdeath_icu*ifr[,2]*ICU+
nu_icuc*propo2*dexo2c*pdeath_icuco*ifr[,2]*ICUC+nu_icuc*(1-propo2)*pdeath_icuc*ifr[,2]*ICUC+
nu_vent*dexv*pdeath_vent*ifr[,2]*Vent+nu_ventc*dexvc*pdeath_ventc*ifr[,2]*VentC +
nu_ventc*dexvc*pdeath_ventc*ifr[,2]*ICUCV+ nu_ventc*report_death_HC*pdeath_vent_hc*ifr[,2]*HCV+
nu_icuc*report_death_HC*propo2*pdeath_icu_hco*ifr[,2]*HCICU+nu_icuc*report_death_HC*(1-propo2)*pdeath_icu_hc*ifr[,2]*HCICU +
mort*H + mort*ICU + mort*ICUC + mort*ICUCV + mort*Vent + mort*VentC + mort*Z +
mort*report_death_HC*HC +mort*report_death_HC*HCICU + mort*report_death_HC*HCV +
report_natdeathI*mort*I + report_natdeathI*mort*QI+ report_natdeathI*mort*E+
report_natdeathI*mort*QE + report_natdeathI*mort*EV+ report_natdeathI*mort*EVR+
report_natdeathI*mort*ER + report_natdeathI*mort*QEV+
report_natdeathI*mort*QEVR + report_natdeathI*mort*QER+
report_natdeathCL*mort*CL + report_natdeathCL*mort*QC + report_natdeathCL*mort*X
dCMCdt <- nusc*propo2*pdeath_hco*ifr[,2]*HC+nusc*(1-propo2)*pdeath_hc*ifr[,2]*HC+
nu_icuc*propo2*dexo2c*pdeath_icuco*ifr[,2]*ICUC+nu_icuc*(1-propo2)*pdeath_icuc*ifr[,2]*ICUC+
nu_ventc*dexvc*pdeath_ventc*ifr[,2]*VentC+nu_ventc*dexvc*pdeath_ventc*ifr[,2]*ICUCV+
mort*HC + mort*ICUC + mort*VentC + mort*ICUCV
dZdt <- gamma*ratetestE*age_testing_vector*(1-ihr[,2])*E+
ratetestI*age_testing_vector*I+
ratetestC*age_testing_vector*CL+
gamma*(1-ihr[,2])*ratetestEV*age_testing_vector*EV+
gamma*(1-ihr[,2])*ratetestEVR*age_testing_vector*EVR+
gamma*(1-ihr[,2])*ratetestER*age_testing_vector*ER+
ratetestHC*age_testing_vector*HC+
ratetestHCICU*age_testing_vector*HCICU+
ratetestHCV*age_testing_vector*HCV-
(1/isolation_days)*Z-mort*Z
dAbdt <- nui*I+nui*X+nui*CL+
nus*propo2*(1-dexo2*pdeath_ho)*ifr[,2]*H+nus*(1-propo2)*(1-pdeath_h)*ifr[,2]*H+
nusc*propo2*(1-pdeath_hco)*ifr[,2]*HC+nusc*(1-propo2)*(1-pdeath_hc)*ifr[,2]*HC+
nusc*propo2*(1-pdeath_icu_hco)*ifr[,2]*HCICU+nusc*(1-propo2)*(1-pdeath_icu_hc)*ifr[,2]*HCICU+
nu_ventc*(1-pdeath_vent_hc)*ifr[,2]*HCV+
nu_icu*propo2*(1-dexo2*pdeath_icuo)*ifr[,2]*ICU+nu_icu*(1-propo2)*(1-pdeath_icu)*ifr[,2]*ICU+
nu_icuc*propo2*(1-dexo2c*pdeath_icuco)*ifr[,2]*ICUC+nu_icuc*(1-propo2)*(1-pdeath_icuc)*ifr[,2]*ICUC+
nu_vent*(1-dexv*pdeath_vent)*ifr[,2]*Vent+
nu_ventc*(1-dexvc*pdeath_ventc)*ifr[,2]*VentC+
nu_ventc*(1-dexvc*pdeath_ventc)*ifr[,2]*ICUCV -
seroneg*Ab - mort*Ab + ageing%*%Ab
# print(paste("QEVR",sum(QEVR)))
# return the rate of change
list(c(S=dSdt,dEdt,dIdt,dRdt,dXdt,dHdt,dHCdt,dCdt,dCMdt,dVdt,dQSdt,dQEdt,dQIdt,dQRdt,dCLdt,dQCdt,dICUdt,dICUCdt,dICUCVdt,
dVentdt,dVentCdt,dCMCdt,dZdt,dEVdt,dERdt,dEVRdt,dVRdt,dQVdt,dQEVdt,dQEVRdt,dQERdt,dQVRdt,dHCICUdt,dHCVdt,dAbdt))
}
)
}
########### RUN BASELINE MODEL - start time for interventions is set to day 1e5, i.e. interventions are always off
Y<-c(initS,initE,initI,initR,initX,initH,initHC,initC,initCM,initV, initQS, initQE, initQI, initQR, initCL, initQC, initICU,
initICUC, initICUCV, initVent, initVentC, initCMC,initZ, initEV, initER, initEVR, initVR,
initQV,initQEV,initQEVR,initQER,initQVR,initHCICU,initHCV,initAb) # initial conditions for the main solution vector
out0 <- ode(y = Y, times = times, method = "euler", hini = 0.05, func = covid, parms = parameters, input=vectors0)
tail(rowSums(out0[,(CMindex+1)]),1) # cumulative mortality
tail(rowSums(out0[,(Sindex+1)]),1)/sum(popstruc[,2]) # cumulative mortality
tail(rowSums(out0[,(Vindex+1)]),1)/sum(popstruc[,2]) # cumulative mortality
tail(rowSums(out0[,(Rindex+1)]),1)/sum(popstruc[,2]) # cumulative mortality
sum(rowSums(out0[,(Iindex+1)])) # cumulative mortality
sum(rowSums(out0[,(Ventindex+1)])) # cumulative mortality
sum(rowSums(out0[,(HCVindex+1)])) # cumulative mortality
sum(rowSums(out0[,(HCICUindex+1)])) # cumulative mortality
sum(rowSums(out0[,(HCindex+1)])) # cumulative mortality
# ############################# PLOT TIME VARYING INTERVENTIONS #####################################
# library(plyr)
# interv_timeseries<-ldply(vectors[c("wah_vector","tb_vector","si_vector","sc_vector","hw_vector","msk_vector")], data.frame)
# interv_timeseries<-cbind(interv_timeseries,rep(1:(length(vectors$si_vector)),6))
# colnames(interv_timeseries)<-c("Intervention","Coverage","Time")
#
# m4 <- interv_timeseries %>%
# # convert state to factor and reverse order of levels
# mutate(Intervention=factor(Intervention,levels=rev(sort(unique(Intervention))))) %>%
# # create a new variable from count
# mutate(covfactor=cut(Coverage,breaks=seq(0,100,by=5),
# labels=c("0-5","5-10","10-15","15-20","20-25","25-30",
# "30-35","35-40","40-45","45-50","50-55","55-60","60-65",
# "65-70","70-75","75-80","80-85","85-90","90-95","95-100"))) %>%
# # change level order
# mutate(covfactor=factor(as.character(covfactor),levels=rev(levels(covfactor))))
# levels(m4$Intervention)<-c("Work from home","Interntional Travel Ban","Self isolation","Screening","Mask wearing","Handwashing")
#
# cols<-c("black",brewer.pal(11,"Spectral"))
# textcol <- "grey40"
# p <- ggplot(m4,aes(x=Time,y=Intervention,fill=covfactor))+
# geom_tile( height = 0.9)+
# guides(fill=guide_legend(title="Coverage (%)"))+
# labs(x="",y="",title="")+
# scale_y_discrete(expand=c(0,0))+
# scale_fill_brewer(palette="Spectral")+
# # coord_fixed()+
# theme_grey(base_size=10)+
# theme(legend.position="right",legend.direction="vertical",
# legend.title=element_text(colour=textcol),
# legend.margin=margin(grid::unit(0,"cm")),
# legend.text=element_text(colour=textcol,size=7,face="bold"),
# legend.key.height=grid::unit(0.8,"cm"),
# legend.key.width=grid::unit(0.2,"cm"),
# axis.text.x=element_text(size=10,colour=textcol),
# axis.text.y=element_text(vjust=0.2,colour=textcol),
# axis.ticks=element_line(size=0.4),
# plot.background=element_blank(),
# panel.border=element_blank(),
# plot.margin=margin(0.7,0.4,0.1,0.2,"cm"),
# plot.title=element_text(colour=textcol,hjust=0,size=14,face="bold"))+
# theme_minimal()
# p
# ################################################################################################
process_ode_outcome <- function(out, iterations,intv_vector){
out_min<-out$min
out_max<-out$max
out_mean<-out$mean
critH<-c()
crit<-c()
critV<-c()
for (i in 1:length(times)){
critH[i]<-min(1-fH((sum(out_mean[i,(Hindex+1)]))+sum(out_mean[i,(ICUCindex+1)])+sum(out_mean[i,(ICUCVindex+1)])),1)
crit[i]<-min(1-fICU((sum(out_mean[i,(ICUindex+1)]))+(sum(out_mean[i,(Ventindex+1)]))+(sum(out_mean[i,(VentCindex+1)]))))
critV[i]<-min(1-fVent((sum(out_mean[i,(Ventindex+1)]))),1)
}
# total population
pop1<-out_mean[,(Sindex+1)]+out_mean[,(Eindex+1)]+out_mean[,(Iindex+1)]+out_mean[,(CLindex+1)]+out_mean[,(Rindex+1)]+
out_mean[,(Xindex+1)]+out_mean[,(Vindex+1)]+out_mean[,(Zindex+1)]+out_mean[,(EVindex+1)]+out_mean[,(ERindex+1)]+out_mean[,(EVRindex+1)]+
out_mean[,(QSindex+1)]+out_mean[,(QEindex+1)]+out_mean[,(QIindex+1)]+out_mean[,(QCindex+1)]+out_mean[,(QRindex+1)]+
out_mean[,(QVindex+1)]+out_mean[,(QEVindex+1)]+out_mean[,(QERindex+1)]+out_mean[,(QVRindex+1)]+out_mean[,(QEVRindex+1)]+
out_mean[,(Hindex+1)]+out_mean[,(HCindex+1)]+out_mean[,(ICUindex+1)]+out_mean[,(ICUCindex+1)]+out_mean[,(ICUCVindex+1)]+
out_mean[,(Ventindex+1)]+out_mean[,(VentCindex+1)]+out_mean[,(HCICUindex+1)]+out_mean[,(HCVindex+1)]
tpop1<-rowSums(pop1)
########################## AB prevalence
ab_age<-out_mean[,(Abindex+1)]
ab_all_ages<-rowSums(out_mean[,(Abindex+1)])
########################## CALCULATE MORTALITY
dexo2_hist <- rep(0,length(times))
dexo2c_hist <- rep(0,length(times))
dexv_hist <- rep(0,length(times))
dexvc_hist <- rep(0,length(times))
for (tt in times) {
if(tt < max(times)){
if(intv_vector$dex[tt*20+1]) {
dexo2_hist[tt+1] <- parameters["dexo2"]
dexo2c_hist[tt+1] <- parameters["dexo2c"]
dexv_hist[tt+1] <- parameters["dexv"]
dexvc_hist[tt+1] <- parameters["dexvc"]
} else {
dexo2_hist[tt+1] <- 1
dexo2c_hist[tt+1] <- 1
dexv_hist[tt+1] <- 1
dexvc_hist[tt+1] <- 1
}
} else {
dexo2_hist[tt+1] <- dexo2_hist[tt]
dexo2c_hist[tt+1] <- dexo2c_hist[tt]
dexv_hist[tt+1] <- dexv_hist[tt]
dexvc_hist[tt+1] <- dexvc_hist[tt]
}
}
cinc_mort_1 <- cumsum(rowSums(parameters["nus"]*parameters["propo2"]*parameters["pdeath_ho"]*dexo2_hist*(out_mean[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_2 <- cumsum(rowSums(parameters["nus"]*(1-parameters["propo2"])*parameters["pdeath_h"]*(out_mean[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_3 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_hco"]*(out_mean[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_4 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_hc"]*(out_mean[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_5 <- cumsum(rowSums(parameters["nu_icu"]*parameters["propo2"]*parameters["pdeath_icuo"]*dexo2_hist*(out_mean[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_6 <- cumsum(rowSums(parameters["nu_icu"]*(1-parameters["propo2"])*parameters["pdeath_icu"]*(out_mean[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_7 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icuco"]*dexo2c_hist*(out_mean[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_8 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icuc"]*(out_mean[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_9 <- cumsum(rowSums(parameters["nu_vent"]*parameters["pdeath_vent"]*dexv_hist*(out_mean[,(Ventindex+1)]%*%ifr[,2])))
cinc_mort_10 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_mean[,(VentCindex+1)]%*%ifr[,2])))
cinc_mort_11 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_mean[,(ICUCVindex+1)]%*%ifr[,2])))
cinc_mort_12 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_icu_hco"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_13 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_icu_hc"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_14 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["report_death_HC"]*parameters["pdeath_vent_hc"]*(out_mean[,(HCVindex+1)]%*%ifr[,2])))
cinc_mort_121 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icu_hco"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_131 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icu_hc"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_141 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_vent_hc"]*(out_mean[,(HCVindex+1)]%*%ifr[,2])))
cinc_mort_H1 <- cinc_mort_1 + cinc_mort_2
cinc_mort_HC1 <- cinc_mort_3 + cinc_mort_4 + cinc_mort_12 + cinc_mort_13 + cinc_mort_14
cinc_mort_ICU1 <- cinc_mort_5 + cinc_mort_6
cinc_mort_ICUC1 <- cinc_mort_7 + cinc_mort_8
cinc_mort_Vent1 <- cinc_mort_9
cinc_mort_VentC1 <- cinc_mort_10
cinc_mort_ICUCV1 <- cinc_mort_11
# all deaths due to covid19 disease - reported + unreported
cinc_mort_all<-cinc_mort_1+cinc_mort_2+cinc_mort_3+cinc_mort_4+cinc_mort_5+cinc_mort_6+
cinc_mort_7+cinc_mort_8+cinc_mort_9+cinc_mort_10+cinc_mort_11+cinc_mort_121+cinc_mort_131+cinc_mort_141
base_mort_H1 <- cumsum(rowSums(out_mean[,(Hindex+1)]%*%mort))
base_mort_HC1 <- cumsum(rowSums(parameters["report_death_HC"]*out_mean[,(HCindex+1)]%*%mort))
base_mort_ICU1 <- cumsum(rowSums(out_mean[,(ICUindex+1)]%*%mort))
base_mort_ICUC1 <- cumsum(rowSums(out_mean[,(ICUCindex+1)]%*%mort))
base_mort_ICUCV1 <- cumsum(rowSums(out_mean[,(ICUCVindex+1)]%*%mort))
base_mort_Vent1 <- cumsum(rowSums(out_mean[,(Ventindex+1)]%*%mort))
base_mort_VentC1 <- cumsum(rowSums(out_mean[,(VentCindex+1)]%*%mort))
base_mort_Z1 <- cumsum(rowSums(out_mean[,(Zindex+1)]%*%mort))
base_mort_HCICU1 <- cumsum(rowSums(parameters["report_death_HC"]*out_mean[,(HCICUindex+1)]%*%mort))
base_mort_HCV1 <- cumsum(rowSums(parameters["report_death_HC"]*out_mean[,(HCVindex+1)]%*%mort))
base_mort_V1 <- cumsum(rowSums(out_mean[,(Vindex+1)]%*%mort))
base_mort_S1 <- cumsum(rowSums(out_mean[,(Sindex+1)]%*%mort))
base_mort_QS1 <- cumsum(rowSums(out_mean[,(QSindex+1)]%*%mort))
base_mort_QR1 <- cumsum(rowSums(out_mean[,(QRindex+1)]%*%mort))
base_mort_R1 <- cumsum(rowSums(out_mean[,(Rindex+1)]%*%mort))
base_mort_QVR1 <- cumsum(rowSums(out_mean[,(QVRindex+1)]%*%mort))
base_mort_VR1 <- cumsum(rowSums(out_mean[,(VRindex+1)]%*%mort))
base_mort_QV1 <- cumsum(rowSums(out_mean[,(QVindex+1)]%*%mort))
base_mort_E1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(Eindex+1)]%*%mort))
base_mort_I1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(Iindex+1)]%*%mort))
base_mort_CL1 <- cumsum(rowSums(parameters["report_natdeathCL"]*out_mean[,(CLindex+1)]%*%mort))
base_mort_X1 <- cumsum(rowSums(parameters["report_natdeathCL"]*out_mean[,(Xindex+1)]%*%mort))
base_mort_QE1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(QEindex+1)]%*%mort))
base_mort_QI1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(QIindex+1)]%*%mort))
base_mort_QC1 <- cumsum(rowSums(parameters["report_natdeathCL"]*out_mean[,(QCindex+1)]%*%mort))
base_mort_ER1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(ERindex+1)]%*%mort))
base_mort_EV1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(EVindex+1)]%*%mort))
base_mort_EVR1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(EVRindex+1)]%*%mort))
base_mort_QEV1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(QEVindex+1)]%*%mort))
base_mort_QER1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(QERindex+1)]%*%mort))
base_mort_QEVR1 <- cumsum(rowSums(parameters["report_natdeathI"]*out_mean[,(QEVRindex+1)]%*%mort))
base_mort_HC11 <- cumsum(rowSums(out_mean[,(HCindex+1)]%*%mort))
base_mort_HCICU11 <- cumsum(rowSums(out_mean[,(HCICUindex+1)]%*%mort))
base_mort_HCV11 <- cumsum(rowSums(out_mean[,(HCVindex+1)]%*%mort))
base_mort_E11 <- cumsum(rowSums(out_mean[,(Eindex+1)]%*%mort))
base_mort_I11 <- cumsum(rowSums(out_mean[,(Iindex+1)]%*%mort))
base_mort_CL11 <- cumsum(rowSums(out_mean[,(CLindex+1)]%*%mort))
base_mort_X11 <- cumsum(rowSums(out_mean[,(Xindex+1)]%*%mort))
base_mort_QE11 <- cumsum(rowSums(out_mean[,(QEindex+1)]%*%mort))
base_mort_QI11 <- cumsum(rowSums(out_mean[,(QIindex+1)]%*%mort))
base_mort_QC11 <- cumsum(rowSums(out_mean[,(QCindex+1)]%*%mort))
base_mort_ER11 <- cumsum(rowSums(out_mean[,(ERindex+1)]%*%mort))
base_mort_EV11 <- cumsum(rowSums(out_mean[,(EVindex+1)]%*%mort))
base_mort_EVR11 <- cumsum(rowSums(out_mean[,(EVRindex+1)]%*%mort))
base_mort_QEV11 <- cumsum(rowSums(out_mean[,(QEVindex+1)]%*%mort))
base_mort_QER11 <- cumsum(rowSums(out_mean[,(QERindex+1)]%*%mort))
base_mort_QEVR11 <- cumsum(rowSums(out_mean[,(QEVRindex+1)]%*%mort))
# all deaths of infected with sars-cov-2 virus - reported + unreported
nat_deaths_inf <- round(base_mort_E11 + base_mort_I11 + base_mort_CL11 + base_mort_X11 +
base_mort_ER11 + base_mort_EV11+ base_mort_EVR11+
base_mort_QE11 + base_mort_QI11 + base_mort_QC11 +
base_mort_QEV11 + base_mort_QER11 + base_mort_QEVR11 + base_mort_Z1+
base_mort_H1+base_mort_HC11+base_mort_ICU1+base_mort_ICUC1+base_mort_ICUCV1+
base_mort_Vent1+base_mort_VentC1+base_mort_HCICU11+base_mort_HCV11)
# Export in a cohesive format ----
results <- list()
results$time <- startdate + times # dates
results$N <- tpop1
## Ab
results$ab_all_ages<-ab_all_ages
results$ab<-ab_age
# Rt/ FOI
dailyinc1<-out$mean_cases # daily incidence
results$Rt <- out$mean_Rt
results$pct_total_pop_infected <- out$mean_infections
results$doubling_time <- round(log(2)*7 / (log(dailyinc1[2+7] / dailyinc1[2])), 2) # (Baseline only) to double the number of infections at inception
results$daily_incidence <- round(dailyinc1) # daily incidence (Reported)
results$daily_total_cases <- round(out$mean_daily_infection) # daily incidence (Reported + Unreported) # daily incidence (Reported + Unreported)
# Hospital requirements
previcureq1<-rowSums(out_mean[,(Hindex+1)])+ rowSums(out_mean[,(ICUCindex+1)])+rowSums(out_mean[,(ICUCVindex+1)]) # surge beds occupancy
previcureq21<-rowSums(out_mean[,(ICUindex+1)])+rowSums(out_mean[,(VentCindex+1)]) # icu beds occupancy
previcureq31<-rowSums(out_mean[,(Ventindex+1)]) # ventilator occupancy
overloadH1<-rowSums(out_mean[,(HCindex+1)]) # requirement for beds
overloadICU1<-rowSums(out_mean[,(ICUCindex+1)])+rowSums(out_mean[,(HCICUindex+1)]) # requirement for icu beds
overloadICUV1<-rowSums(out_mean[,(ICUCVindex+1)]) # requirement for ventilators
overloadVent1<-rowSums(out_mean[,(VentCindex+1)])+rowSums(out_mean[,(HCVindex+1)]) # requirement for ventilators
results$required_beds <- round(previcureq1) # required beds
results$saturation <- parameters["beds_available"] # saturation
results$hospital_surge_beds <- round(previcureq1)
results$icu_beds <- round(previcureq21)
results$ventilators <- round(previcureq31)
results$normal_bed_requirement <- round(rowSums(out_mean[,(Hindex+1)])+overloadH1) #real required beds. previcureq1 above is the occupancy
results$icu_bed_requirement <- round(rowSums(out_mean[,(ICUindex+1)])+overloadICU1)
results$icu_ventilator_requirement <- round(rowSums(out_mean[,(Ventindex+1)])+overloadICUV1+overloadVent1)
### MORTALITY
results$cum_mortality <- round(rowSums(out_mean[,(CMindex+1)])) # cumulative mortality
results$deaths_from_covid<-last(cinc_mort_all)
results$deaths_with_covid<-last(nat_deaths_inf)
results$death_natural_non_exposed <- round(base_mort_S1+base_mort_V1+base_mort_QS1)
results$death_natural_exposed <- round(base_mort_E1 + base_mort_I1 + base_mort_CL1 + base_mort_X1 +
base_mort_R1+ base_mort_ER1 + base_mort_EV1+ base_mort_EVR1+
base_mort_QE1 + base_mort_QI1 + base_mort_QC1 + base_mort_QR1 +
base_mort_QEV1 + base_mort_QER1 + base_mort_QEVR1 + base_mort_QVR1 +
base_mort_H1+base_mort_HC1+base_mort_ICU1+base_mort_ICUC1+base_mort_ICUCV1+
base_mort_Vent1+base_mort_VentC1+base_mort_HCICU1+base_mort_HCV1)
results$death_treated_hospital <- round(cinc_mort_H1)
results$death_treated_icu <- round(cinc_mort_ICU1)
results$death_treated_ventilator <- round(cinc_mort_Vent1)
results$death_untreated_hospital <- round(cinc_mort_HC1)
results$death_untreated_icu <- round(cinc_mort_ICUC1)
results$death_untreated_ventilator <- round(cinc_mort_VentC1)+round(cinc_mort_ICUCV1)
results$attributable_deaths <- results$death_treated_hospital + results$death_treated_icu + results$death_treated_ventilator +
results$death_untreated_hospital + results$death_untreated_icu + results$death_untreated_ventilator
results$attributable_deaths_end <- last(results$attributable_deaths)
results$total_deaths <- results$attributable_deaths + results$death_natural_non_exposed + results$death_natural_exposed
results$total_deaths_end <- last(results$total_deaths)
results$total_reported_deaths_end <- last(results$cum_mortality)
# ## AGE DEPENDENT MORTALITY
cinc_mort_H1 <- parameters["nus"]*parameters["propo2"]*parameters["pdeath_ho"]*dexo2_hist*(out_mean[,(Hindex+1)]%*%ifr[,2])+
parameters["nus"]*(1-parameters["propo2"])*parameters["pdeath_h"]*(out_mean[,(Hindex+1)]%*%ifr[,2])
cinc_mort_HC1 <- parameters["nusc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_hco"]*(out_mean[,(HCindex+1)]%*%ifr[,2])+
parameters["nusc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_hc"]*(out_mean[,(HCindex+1)]%*%ifr[,2])
cinc_mort_ICU1 <- parameters["nu_icu"]*parameters["propo2"]*parameters["pdeath_icuo"]*dexo2_hist*(out_mean[,(ICUindex+1)]%*%ifr[,2])+
parameters["nu_icu"]*(1-parameters["propo2"])*parameters["pdeath_icu"]*(out_mean[,(ICUindex+1)]%*%ifr[,2])
cinc_mort_ICUC1 <- parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icuco"]*dexo2c_hist*(out_mean[,(ICUCindex+1)]%*%ifr[,2])+
parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icuc"]*(out_mean[,(ICUCindex+1)]%*%ifr[,2])
cinc_mort_Vent1 <- parameters["nu_vent"]*parameters["pdeath_vent"]*dexv_hist*(out_mean[,(Ventindex+1)]%*%ifr[,2])
cinc_mort_VentC1 <- parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_mean[,(VentCindex+1)]%*%ifr[,2])
cinc_mort_ICUCV1 <- parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_mean[,(ICUCVindex+1)]%*%ifr[,2])
cinc_mort_HCICU1 <- parameters["nu_icuc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_icu_hco"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])+
parameters["nu_icuc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_icu_hc"]*(out_mean[,(HCICUindex+1)]%*%ifr[,2])
cinc_mort_HCV1 <- parameters["nu_ventc"]*parameters["report_death_HC"]*parameters["pdeath_vent_hc"]*(out_mean[,(HCVindex+1)]%*%ifr[,2])
totage1<-as.data.frame(cinc_mort_H1+cinc_mort_HC1+cinc_mort_ICU1+cinc_mort_ICUC1+
cinc_mort_Vent1+cinc_mort_VentC1+cinc_mort_ICUCV1+cinc_mort_HCICU1+cinc_mort_HCV1)
basemort_H1<-(out_mean[,(Hindex+1)])
basemort_HC1<-(out_mean[,(HCindex+1)])
basemort_ICU1<-(out_mean[,(ICUindex+1)])
basemort_ICUC1<-(out_mean[,(ICUCindex+1)])
basemort_ICUCV1<-(out_mean[,(ICUCVindex+1)])
basemort_Vent1<-(out_mean[,(Ventindex+1)])
basemort_VentC1<-(out_mean[,(VentCindex+1)])
basemort_HCICU1<-(out_mean[,(HCICUindex+1)])
basemort_HCV1<-(out_mean[,(HCVindex+1)])
totbase1<-as.data.frame(basemort_H1+basemort_HC1+basemort_ICU1+basemort_ICUC1+basemort_ICUCV1+
basemort_Vent1+basemort_VentC1+basemort_HCICU1+basemort_HCV1)
tc<-c()
for (i in 1:dim(cinc_mort_H1)[1]) {
for (j in 1:dim(cinc_mort_H1)[2]) {
tc<-rbind(tc,c(i, j, totage1[i,j]*ifr[j,2]+totbase1[i,j]*mort[j]))
}
}
tc<-as.data.frame(tc)
colnames(tc)<-c("Day","Age","value")
results$tc <- tc %>%
mutate(Date = startdate + Day,
age_cat = case_when(
Age >= 1 & Age <= 6 ~ "<= 30 y.o.",
Age > 6 & Age <= 8 ~ "30-40 y.o.",
Age > 8 & Age <= 10 ~ "40-50 y.o.",
Age > 10 & Age <= 12 ~ "50-60 y.o.",
Age > 12 & Age <= 14 ~ "60-70 y.o.",
Age >= 15 ~ ">= 70 y.o.")) %>%
mutate(age_cat = factor(age_cat, levels = rev(c("<= 30 y.o.", "30-40 y.o.",
"40-50 y.o.", "50-60 y.o.", "60-70 y.o.", ">= 70 y.o."))))
mortality_lag <- data.frame(Age = popstruc$agefloor)
if(nrow(out_mean) >= 30) mortality_lag <- bind_cols(mortality_lag,
data.frame(day30 = out_mean[30,CMindex+1]/out_mean[30,Cindex+1]) %>%
mutate(day30 = ifelse(is.infinite(day30), 0, day30)))
if(nrow(out_mean) >= 60) mortality_lag <- bind_cols(mortality_lag,
data.frame(day60 = out_mean[60,CMindex+1]/out_mean[60,Cindex+1]) %>%
mutate(day60 = ifelse(is.infinite(day60), 0, day60)))
if(nrow(out_mean) >= 90) mortality_lag <- bind_cols(mortality_lag,
data.frame(day90 = out_mean[90,CMindex+1]/out_mean[90,Cindex+1]) %>%
mutate(day90 = ifelse(is.infinite(day90), 0, day90)))
if(nrow(out_mean) >= 120) mortality_lag <- bind_cols(mortality_lag,
data.frame(day120 = out_mean[120,CMindex+1]/out_mean[120,Cindex+1]) %>%
mutate(day120 = ifelse(is.infinite(day120), 0, day120)))
results$mortality_lag <- mortality_lag
if(iterations>1){
cinc_mort_1 <- cumsum(rowSums(parameters["nus"]*parameters["propo2"]*parameters["pdeath_ho"]*dexo2_hist*(out_min[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_2 <- cumsum(rowSums(parameters["nus"]*(1-parameters["propo2"])*parameters["pdeath_h"]*(out_min[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_3 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_hco"]*(out_min[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_4 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_hc"]*(out_min[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_5 <- cumsum(rowSums(parameters["nu_icu"]*parameters["propo2"]*parameters["pdeath_icuo"]*dexo2_hist*(out_min[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_6 <- cumsum(rowSums(parameters["nu_icu"]*(1-parameters["propo2"])*parameters["pdeath_icu"]*(out_min[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_7 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icuco"]*dexo2c_hist*(out_min[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_8 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icuc"]*(out_min[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_9 <- cumsum(rowSums(parameters["nu_vent"]*parameters["pdeath_vent"]*dexv_hist*(out_min[,(Ventindex+1)]%*%ifr[,2])))
cinc_mort_10 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_min[,(VentCindex+1)]%*%ifr[,2])))
cinc_mort_11 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_min[,(ICUCVindex+1)]%*%ifr[,2])))
cinc_mort_121 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icu_hco"]*(out_min[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_131 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icu_hc"]*(out_min[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_141 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_vent_hc"]*(out_min[,(HCVindex+1)]%*%ifr[,2])))
cinc_mort_all_min<-cinc_mort_1+cinc_mort_2+cinc_mort_3+cinc_mort_4+cinc_mort_5+cinc_mort_6+
cinc_mort_7+cinc_mort_8+cinc_mort_9+cinc_mort_10+cinc_mort_11+cinc_mort_121+cinc_mort_131+cinc_mort_141
cinc_mort_1 <- cumsum(rowSums(parameters["nus"]*parameters["propo2"]*parameters["pdeath_ho"]*dexo2_hist*(out_max[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_2 <- cumsum(rowSums(parameters["nus"]*(1-parameters["propo2"])*parameters["pdeath_h"]*(out_max[,(Hindex+1)]%*%ifr[,2])))
cinc_mort_3 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*parameters["propo2"]*parameters["pdeath_hco"]*(out_max[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_4 <- cumsum(rowSums(parameters["nusc"]*parameters["report_death_HC"]*(1-parameters["propo2"])*parameters["pdeath_hc"]*(out_max[,(HCindex+1)]%*%ifr[,2])))
cinc_mort_5 <- cumsum(rowSums(parameters["nu_icu"]*parameters["propo2"]*parameters["pdeath_icuo"]*dexo2_hist*(out_max[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_6 <- cumsum(rowSums(parameters["nu_icu"]*(1-parameters["propo2"])*parameters["pdeath_icu"]*(out_max[,(ICUindex+1)]%*%ifr[,2])))
cinc_mort_7 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icuco"]*dexo2c_hist*(out_max[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_8 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icuc"]*(out_max[,(ICUCindex+1)]%*%ifr[,2])))
cinc_mort_9 <- cumsum(rowSums(parameters["nu_vent"]*parameters["pdeath_vent"]*dexv_hist*(out_max[,(Ventindex+1)]%*%ifr[,2])))
cinc_mort_10 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_max[,(VentCindex+1)]%*%ifr[,2])))
cinc_mort_11 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_ventc"]*dexvc_hist*(out_max[,(ICUCVindex+1)]%*%ifr[,2])))
cinc_mort_121 <- cumsum(rowSums(parameters["nu_icuc"]*parameters["propo2"]*parameters["pdeath_icu_hco"]*(out_max[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_131 <- cumsum(rowSums(parameters["nu_icuc"]*(1-parameters["propo2"])*parameters["pdeath_icu_hc"]*(out_max[,(HCICUindex+1)]%*%ifr[,2])))
cinc_mort_141 <- cumsum(rowSums(parameters["nu_ventc"]*parameters["pdeath_vent_hc"]*(out_max[,(HCVindex+1)]%*%ifr[,2])))
cinc_mort_all_max<-cinc_mort_1+cinc_mort_2+cinc_mort_3+cinc_mort_4+cinc_mort_5+cinc_mort_6+
cinc_mort_7+cinc_mort_8+cinc_mort_9+cinc_mort_10+cinc_mort_11+cinc_mort_121+cinc_mort_131+cinc_mort_141
base_mort_H1 <- cumsum(rowSums(out_min[,(Hindex+1)]%*%mort))
base_mort_ICU1 <- cumsum(rowSums(out_min[,(ICUindex+1)]%*%mort))
base_mort_ICUC1 <- cumsum(rowSums(out_min[,(ICUCindex+1)]%*%mort))
base_mort_ICUCV1 <- cumsum(rowSums(out_min[,(ICUCVindex+1)]%*%mort))
base_mort_Vent1 <- cumsum(rowSums(out_min[,(Ventindex+1)]%*%mort))
base_mort_VentC1 <- cumsum(rowSums(out_min[,(VentCindex+1)]%*%mort))
base_mort_Z1 <- cumsum(rowSums(out_min[,(Zindex+1)]%*%mort))
base_mort_HC11 <- cumsum(rowSums(out_min[,(HCindex+1)]%*%mort))
base_mort_HCICU11 <- cumsum(rowSums(out_min[,(HCICUindex+1)]%*%mort))
base_mort_HCV11 <- cumsum(rowSums(out_min[,(HCVindex+1)]%*%mort))
base_mort_E11 <- cumsum(rowSums(out_min[,(Eindex+1)]%*%mort))
base_mort_I11 <- cumsum(rowSums(out_min[,(Iindex+1)]%*%mort))
base_mort_CL11 <- cumsum(rowSums(out_min[,(CLindex+1)]%*%mort))
base_mort_X11 <- cumsum(rowSums(out_min[,(Xindex+1)]%*%mort))
base_mort_QE11 <- cumsum(rowSums(out_min[,(QEindex+1)]%*%mort))
base_mort_QI11 <- cumsum(rowSums(out_min[,(QIindex+1)]%*%mort))
base_mort_QC11 <- cumsum(rowSums(out_min[,(QCindex+1)]%*%mort))
base_mort_ER11 <- cumsum(rowSums(out_min[,(ERindex+1)]%*%mort))
base_mort_EV11 <- cumsum(rowSums(out_min[,(EVindex+1)]%*%mort))
base_mort_EVR11 <- cumsum(rowSums(out_min[,(EVRindex+1)]%*%mort))
base_mort_QEV11 <- cumsum(rowSums(out_min[,(QEVindex+1)]%*%mort))
base_mort_QER11 <- cumsum(rowSums(out_min[,(QERindex+1)]%*%mort))
base_mort_QEVR11 <- cumsum(rowSums(out_min[,(QEVRindex+1)]%*%mort))
nat_deaths_inf_min <- last(round(base_mort_E11 + base_mort_I11 + base_mort_CL11 + base_mort_X11 +
base_mort_ER11 + base_mort_EV11+ base_mort_EVR11+
base_mort_QE11 + base_mort_QI11 + base_mort_QC11 +
base_mort_QEV11 + base_mort_QER11 + base_mort_QEVR11 + base_mort_Z1+
base_mort_H1+base_mort_HC11+base_mort_ICU1+base_mort_ICUC1+base_mort_ICUCV1+
base_mort_Vent1+base_mort_VentC1+base_mort_HCICU11+base_mort_HCV11))
base_mort_H1 <- cumsum(rowSums(out_max[,(Hindex+1)]%*%mort))
base_mort_ICU1 <- cumsum(rowSums(out_max[,(ICUindex+1)]%*%mort))
base_mort_ICUC1 <- cumsum(rowSums(out_max[,(ICUCindex+1)]%*%mort))
base_mort_ICUCV1 <- cumsum(rowSums(out_max[,(ICUCVindex+1)]%*%mort))
base_mort_Vent1 <- cumsum(rowSums(out_max[,(Ventindex+1)]%*%mort))
base_mort_VentC1 <- cumsum(rowSums(out_max[,(VentCindex+1)]%*%mort))
base_mort_Z1 <- cumsum(rowSums(out_max[,(Zindex+1)]%*%mort))
base_mort_HC11 <- cumsum(rowSums(out_max[,(HCindex+1)]%*%mort))
base_mort_HCICU11 <- cumsum(rowSums(out_max[,(HCICUindex+1)]%*%mort))
base_mort_HCV11 <- cumsum(rowSums(out_max[,(HCVindex+1)]%*%mort))
base_mort_E11 <- cumsum(rowSums(out_max[,(Eindex+1)]%*%mort))
base_mort_I11 <- cumsum(rowSums(out_max[,(Iindex+1)]%*%mort))
base_mort_CL11 <- cumsum(rowSums(out_max[,(CLindex+1)]%*%mort))
base_mort_X11 <- cumsum(rowSums(out_max[,(Xindex+1)]%*%mort))
base_mort_QE11 <- cumsum(rowSums(out_max[,(QEindex+1)]%*%mort))
base_mort_QI11 <- cumsum(rowSums(out_max[,(QIindex+1)]%*%mort))
base_mort_QC11 <- cumsum(rowSums(out_max[,(QCindex+1)]%*%mort))
base_mort_ER11 <- cumsum(rowSums(out_max[,(ERindex+1)]%*%mort))
base_mort_EV11 <- cumsum(rowSums(out_max[,(EVindex+1)]%*%mort))
base_mort_EVR11 <- cumsum(rowSums(out_max[,(EVRindex+1)]%*%mort))
base_mort_QEV11 <- cumsum(rowSums(out_max[,(QEVindex+1)]%*%mort))
base_mort_QER11 <- cumsum(rowSums(out_max[,(QERindex+1)]%*%mort))
base_mort_QEVR11 <- cumsum(rowSums(out_max[,(QEVRindex+1)]%*%mort))
nat_deaths_inf_max<- last(round(base_mort_E11 + base_mort_I11 + base_mort_CL11 + base_mort_X11 +
base_mort_ER11 + base_mort_EV11+ base_mort_EVR11+
base_mort_QE11 + base_mort_QI11 + base_mort_QC11 +
base_mort_QEV11 + base_mort_QER11 + base_mort_QEVR11 + base_mort_Z1+
base_mort_H1+base_mort_HC11+base_mort_ICU1+base_mort_ICUC1+base_mort_ICUCV1+
base_mort_Vent1+base_mort_VentC1+base_mort_HCICU11+base_mort_HCV11))
previcureq1_max<-rowSums(out_max[,(Hindex+1)])+ rowSums(out_max[,(ICUCindex+1)])+rowSums(out_max[,(ICUCVindex+1)]) # surge beds occupancy
previcureq21_max<-rowSums(out_max[,(ICUindex+1)])+rowSums(out_max[,(VentCindex+1)]) # icu beds occupancy
previcureq31_max<-rowSums(out_max[,(Ventindex+1)]) # ventilator occupancy
cmortality1_max<-rowSums(out_max[,(CMindex+1)]) # cumulative mortality
overloadH1_max<-rowSums(out_max[,(HCindex+1)]) # requirement for beds
overloadICU1_max<-rowSums(out_max[,(ICUCindex+1)])+ rowSums(out_max[,(HCICUindex+1)]) # requirement for icu beds
overloadICUV1_max<-rowSums(out_max[,(ICUCVindex+1)])+ rowSums(out_max[,(HCVindex+1)]) # requirement for ventilators
overloadVent1_max<-rowSums(out_max[,(VentCindex+1)]) # requirement for ventilators
ccases1_max<-rowSums(out_max[,(Cindex+1)]) # cumulative cases
reqsurge1_max<-rowSums(out_max[,(Hindex+1)])+overloadH1 # surge beds total requirements
reqicu1_max<-rowSums(out_max[,(ICUindex+1)])+overloadICU1 # ICU beds total requirements
reqvent1_max<-rowSums(out_max[,(Ventindex+1)])+overloadICUV1+overloadVent1 # ventilator beds total requirements
previcureq1_min<-rowSums(out_min[,(Hindex+1)])+rowSums(out_min[,(ICUCindex+1)])+rowSums(out_min[,(ICUCVindex+1)]) # surge beds occupancy
previcureq21_min<-rowSums(out_min[,(ICUindex+1)])+rowSums(out_min[,(VentCindex+1)]) # icu beds occupancy
previcureq31_min<-rowSums(out_min[,(Ventindex+1)]) # ventilator occupancy
cmortality1_min<-rowSums(out_min[,(CMindex+1)]) # cumulative mortality
overloadH1_min<-rowSums(out_min[,(HCindex+1)]) # requirement for beds
overloadICU1_min<-rowSums(out_min[,(ICUCindex+1)]) # requirement for icu beds
overloadICUV1_min<-rowSums(out_min[,(ICUCVindex+1)]) # requirement for ventilators
overloadVent1_min<-rowSums(out_min[,(VentCindex+1)]) # requirement for ventilators
ccases1_min<-rowSums(out_min[,(Cindex+1)]) # cumulative cases
reqsurge1_min<-rowSums(out_min[,(Hindex+1)])+overloadH1 # surge beds total requirements
reqicu1_min<-rowSums(out_min[,(ICUindex+1)])+overloadICU1 # ICU beds total requirements
reqvent1_min<-rowSums(out_min[,(Ventindex+1)])+overloadICUV1+overloadVent1 # ventilator beds total requirements
results$Rt_max <- out$max_Rt
results$Rt_min <- out$min_Rt
results$daily_incidence_max <- out$max_cases
results$daily_incidence_min <- out$min_cases
results$deaths_from_covid_min<-last(cinc_mort_all_min)
results$deaths_from_covid_max<-last(cinc_mort_all_max)
results$deaths_with_covid_min<-last(nat_deaths_inf_min)
results$deaths_with_covid_max<-last(nat_deaths_inf_max)
results$daily_total_cases_max <- out$max_daily_infection
results$daily_total_cases_min <- out$min_daily_infection
results$total_reported_deaths_end_min <- last(cmortality1_min)
results$total_reported_deaths_end_max <- last(cmortality1_max)
results$pct_total_pop_infected_min <- out$min_infections # proportion of the population that has been infected at the end of the simulation
results$pct_total_pop_infected_max <- out$max_infections # proportion of the population that has been infected at the end of the simulation
}
return(results)
}
multi_runs<-function(Y,times,parameters,input,iterations,noise,confidence,fit,fit_mat){
results <- list()
aux<-array(0, dim=c(length(times),35*A+1,iterations))
results$mean<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$min<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$max<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$mean_cases<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$min_cases<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$max_cases<-matrix(0,nrow = length(times),ncol = 35*A+1)
results$mean_cum_cases<-matrix(0,nrow = length(times),ncol = 1)
results$min_cum_cases<-matrix(0,nrow = length(times),ncol = 1)
results$max_cum_cases<-matrix(0,nrow = length(times),ncol = 1)
results$mean_daily_infection<-matrix(0,nrow = length(times),ncol = 1)
results$min_daily_infection<-matrix(0,nrow = length(times),ncol = 1)
results$max_daily_infection<-matrix(0,nrow = length(times),ncol = 1)
results$mean_ab<-matrix(0,nrow = length(times),ncol = 1)
results$min_ab<-matrix(0,nrow = length(times),ncol = 1)
results$max_ab<-matrix(0,nrow = length(times),ncol = 1)
cases<-matrix(0, nrow=length(times),ncol=iterations)
cum_cases<-matrix(0, nrow=length(times),ncol=iterations)
day_infections<-matrix(0, nrow=length(times),ncol=iterations)
Rt_aux<-matrix(0, nrow=length(times),ncol=iterations)
infections<-matrix(0, nrow=iterations,ncol=1)
Rt <- NULL
print(iterations)
param_vector<-parameters
if(iterations>1){
for (i in 1:iterations){
print(i)
param_vector[parameters_noise]<-parameters[parameters_noise]+rnorm(length(parameters_noise),mean=0,sd=noise*abs(parameters[parameters_noise]))
if(fit){
param_vector[parameters_fit]<-fit_mat[,floor(runif(1)*(length(fit_mat[1,])-1))+1]
initE[aci]<-round(sum(popstruc[,2])/param_vector["init"])
initS<-popstruc[,2]-initE-initI-initCL-initR-initX-initZ-initV-initH-initHC-initICU-initICUC-initICUCV-initVent-initVentC-
initQS-initQE-initQI-initQR-initQC-initEV-initER-initEVR-initVR-initQV-initQEV-initQEVR-initQER-initQVR-
initHCICU-initHCV
Y<-c(initS,initE,initI,initR,initX,initH,initHC,initC,initCM,initV, initQS, initQE, initQI, initQR, initCL, initQC, initICU,
initICUC, initICUCV, initVent, initVentC, initCMC,initZ, initEV, initER, initEVR, initVR,
initQV,initQEV,initQEVR,initQER,initQVR,initHCICU,initHCV,initAb)
}
out0 <- ode(y = Y, times = times, method = "euler", hini = 0.05, func = covid, parms = param_vector, input=input)
aux[,,i]<-out0
critH<-c()
crit<-c()
critV<-c()
for (ii in 1:length(times)){
critH[ii]<-min(1-fH((sum(out0[ii,(Hindex+1)]))+sum(out0[ii,(ICUCindex+1)])+sum(out0[ii,(ICUCVindex+1)])),1)
crit[ii]<-min(1-fICU((sum(out0[ii,(ICUindex+1)]))+(sum(out0[ii,(Ventindex+1)]))+(sum(out0[ii,(VentCindex+1)]))),1)
critV[ii]<-min(1-fVent((sum(out0[ii,(Ventindex+1)]))),1)
}
# daily incidence
incidence<-param_vector["report"]*param_vector["gamma"]*(1-param_vector["pclin"])*out0[,(Eindex+1)]%*%(1-ihr[,2])+
param_vector["reportc"]*param_vector["gamma"]*param_vector["pclin"]*out0[,(Eindex+1)]%*%(1-ihr[,2])+
param_vector["report"]*param_vector["gamma"]*(1-param_vector["pclin"])*out0[,(QEindex+1)]%*%(1-ihr[,2])+
param_vector["reportc"]*param_vector["gamma"]*param_vector["pclin"]*out0[,(QEindex+1)]%*%(1-ihr[,2])+
param_vector["report_v"]*param_vector["gamma"]*(1-param_vector["pclin_v"])*out0[,(EVindex+1)]%*%(1-param_vector["sigmaEV"]*ihr[,2])+
param_vector["report_cv"]*param_vector["gamma"]*param_vector["pclin_v"]*out0[,(EVindex+1)]%*%(1-param_vector["sigmaEV"]*ihr[,2])+
param_vector["report_vr"]*param_vector["gamma"]*(1-param_vector["pclin_vr"])*out0[,(EVRindex+1)]%*%(1-param_vector["sigmaEVR"]*ihr[,2])+
param_vector["report_cvr"]*param_vector["gamma"]*param_vector["pclin_vr"]*out0[,(EVRindex+1)]%*%(1-param_vector["sigmaEVR"]*ihr[,2])+
param_vector["report_r"]*param_vector["gamma"]*(1-param_vector["pclin_r"])*out0[,(ERindex+1)]%*%(1-param_vector["sigmaER"]*ihr[,2])+
param_vector["report_cr"]*param_vector["gamma"]*param_vector["pclin_r"]*out0[,(ERindex+1)]%*%(1-param_vector["sigmaER"]*ihr[,2])
incidenceh<- param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu"])*param_vector["reporth"]+
param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu"])*(1-param_vector["reporth"])*param_vector["reporth_g"]+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu"])*param_vector["reporth"]+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu"])*(1-param_vector["reporth"])*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaEV"]*out0[,(EVindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu_v"])*param_vector["reporth"]+
param_vector["gamma"]*param_vector["sigmaEVR"]*out0[,(EVRindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu_vr"])*param_vector["reporth"]+
param_vector["gamma"]*param_vector["sigmaER"]*out0[,(ERindex+1)]%*%ihr[,2]*(1-critH)*(1-param_vector["prob_icu_r"])*param_vector["reporth"]+
param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*critH*param_vector["reporth_g"]*(1-param_vector["prob_icu"])+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*critH*param_vector["reporth_g"]*(1-param_vector["prob_icu"])+
param_vector["gamma"]*param_vector["sigmaEV"]*out0[,(EVindex+1)]%*%ihr[,2]*critH*param_vector["reporth_g"]*(1-param_vector["prob_icu_v"])+
param_vector["gamma"]*param_vector["sigmaEVR"]*out0[,(EVRindex+1)]%*%ihr[,2]*critH*param_vector["reporth_g"]*(1-param_vector["prob_icu_vr"])+
param_vector["gamma"]*param_vector["sigmaER"]*out0[,(ERindex+1)]%*%ihr[,2]*critH*param_vector["reporth_g"]*(1-param_vector["prob_icu_r"])+
#ICU
param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*(1-crit)*param_vector["reporth_ICU"]+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*(1-crit)*param_vector["reporth_ICU"]+
param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*crit*param_vector["reporth_ICU"]*param_vector["reporth_g"]+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*crit*param_vector["reporth_ICU"]*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaEV"]*out0[,(EVindex+1)]%*%ihr[,2]*(1-crit)*param_vector["prob_icu_v"]*param_vector["reporth_ICU"]+
param_vector["gamma"]*param_vector["sigmaEVR"]*out0[,(EVRindex+1)]%*%ihr[,2]*(1-crit)*param_vector["prob_icu_vr"]*param_vector["reporth_ICU"]+
param_vector["gamma"]*param_vector["sigmaER"]*out0[,(ERindex+1)]%*%ihr[,2]*(1-crit)*param_vector["prob_icu_r"]*param_vector["reporth_ICU"]+
param_vector["gamma"]*param_vector["sigmaEV"]*out0[,(EVindex+1)]%*%ihr[,2]*crit*param_vector["prob_icu_v"]*param_vector["reporth_ICU"]*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaEVR"]*out0[,(EVRindex+1)]%*%ihr[,2]*crit*param_vector["prob_icu_vr"]*param_vector["reporth_ICU"]*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaER"]*out0[,(ERindex+1)]%*%ihr[,2]*crit*param_vector["prob_icu_r"]*param_vector["reporth_ICU"]*param_vector["reporth_g"]+
param_vector["gamma"]*out0[,(Eindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*(1-param_vector["reporth_ICU"])*param_vector["reporth_g"]+
param_vector["gamma"]*out0[,(QEindex+1)]%*%ihr[,2]*param_vector["prob_icu"]*(1-param_vector["reporth_ICU"])*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaEV"]*out0[,(EVindex+1)]%*%ihr[,2]*param_vector["prob_icu_v"]*(1-param_vector["reporth_ICU"])*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaEVR"]*out0[,(EVRindex+1)]%*%ihr[,2]*param_vector["prob_icu_vr"]*(1-param_vector["reporth_ICU"])*param_vector["reporth_g"]+
param_vector["gamma"]*param_vector["sigmaER"]*out0[,(ERindex+1)]%*%ihr[,2]*param_vector["prob_icu_r"]*(1-param_vector["reporth_ICU"])*param_vector["reporth_g"]
cases[,i]<-(rowSums(incidence)+rowSums(incidenceh)) # daily incidence cases
cum_cases[,i]<-colSums(incidence)+colSums(incidenceh) # cumulative incidence cases
day_infections[,i]<- round(rowSums(param_vector["gamma"]*out0[,(Eindex+1)]+
param_vector["gamma"]*out0[,(QEindex+1)]+
param_vector["gamma"]*out0[,(EVindex+1)]+
param_vector["gamma"]*out0[,(EVRindex+1)]+
param_vector["gamma"]*out0[,(ERindex+1)]))
# daily infections
infections[i] <- round(100*last(cumsum(day_infections[,i]))/sum(popstruc[,2]), 1) # proportion of the population that has been infected at the end of the simulation
for (w in (ceiling(1/param_vector["nui"])+1):length(times)){
Rt_aux[w,i]<-cumsum(sum(param_vector["gamma"]*out0[w,(Eindex+1)]))/cumsum(sum(param_vector["gamma"]*out0[(w-1/param_vector["nui"]),(Eindex+1)]))
if(Rt_aux[w,i] >= 7) {Rt_aux[w,i] <- NA}
}
}
qq <- quantile(infections, c(confidence, 0.5, (1-confidence)))
results$mean_infections<-qq[2]
results$min_infections<-qq[1]
results$max_infections<-qq[3]
for(i in 1:length(out0[,1])){
qq <- quantile(cases[i,], c(confidence, 0.5, (1-confidence)))
results$mean_cases[i]<-qq[2]
results$min_cases[i]<-qq[1]
results$max_cases[i]<-qq[3]
qq <- quantile(cum_cases[i,], c(confidence, 0.5, (1-confidence)))
results$mean_cum_cases[i]<-qq[2]
results$min_cum_cases[i]<-qq[1]
results$max_cum_cases[i]<-qq[3]
qq <- quantile(day_infections[i,], c(confidence, 0.5, (1-confidence)))
results$mean_daily_infection[i]<-qq[2]
results$min_daily_infection[i]<-qq[1]
results$max_daily_infection[i]<-qq[3]
qq <- quantile(Rt_aux[i,], c(confidence, 0.5, (1-confidence)),na.rm = T)
results$mean_Rt[i]<-qq[2]
results$min_Rt[i]<-qq[1]
results$max_Rt[i]<-qq[3]
for (j in 1:length(out0[1,])){
qq <- quantile(aux[i,j,], c(confidence, 0.5, (1-confidence)))
results$mean[i,j]<-qq[2]
results$min[i,j]<-qq[1]
results$max[i,j]<-qq[3]
}
}
}else{
results$mean <- ode(y = Y, times = times, method = "euler", hini = 0.05, func = covid, parms = parameters, input=input)
}
return(results)
}
out0<-multi_runs(Y, times, parameters, vectors0, iterations, noise, confidence,0,fit_mat)
out0$min_infections
out0$max_infections
plot(times,rowSums(out0$mean[,Iindex+1]),type = 'l')
polygon(c(times, rev(times)), c(rowSums(out0$max[,Iindex+1]), rev(rowSums(out0$min[,Iindex+1]))),
col=rgb(0, 0, 0,0.25), border = NA)
plot(times,out0$mean_Rt,type = 'l')
polygon(c(times, rev(times)), c(out0$max_Rt, rev(out0$min_Rt)),
col=rgb(0, 0, 0,0.25), border = NA)
simul_baseline <- process_ode_outcome(out0,iterations,vectors0)
# # write.csv(simul_baseline, paste0(hilo,"_baseline_",gsub(":|-","",Sys.time()),".csv"))
#
#future interventions
#extend travel ban, quarantine, hand washing, cocooning the elderly until 1st July
out <-multi_runs(Y, times, parameters, vectors, iterations, noise, confidence,0,fit_mat)
simul_interventions <- process_ode_outcome(out,iterations,vectors)
# write.csv(simul_interventions, paste0(hilo,"_futureIntv_",gsub(":|-","",Sys.time()),".csv"))
############# PLOTTING
# Fitting tab
# fitting the intervention lines to the data to account for any historical interventions
time<-as.Date(out0$mean[,1]+startdate)
par(mfrow=c(1,2))
# set up the axis limits
xmin<-min(as.Date(cases_rv[,1]))
xmax<-max(as.Date(cases_rv[,1]))
ymax<-max(cases_rv[,2],na.rm = T)
xtick<-seq(xmin, xmax, by=7)
plot(time,rowSums(simul_interventions$daily_incidence),type='l',lwd=3,
main="New Reported Cases", xlab="Date", ylab="Cases per day",
xlim=c(xmin,xmax), ylim=c(0,ymax), col='blue',xaxt="n")
axis(side=1, labels = FALSE)
text(x=xtick, y=-250, labels = format(xtick,"%b-%d"), srt = 0, xpd = TRUE)
points(as.Date(cases_rv[,1]),cases_rv[,2],pch=19,col='red')
# reset the maximum to the cumulative mortality
ymax<-max(cases_rv[,3],na.rm = T)
plot(time,simul_interventions$cum_mortality,type='l',lwd=3,
main="Cumulative Mortality", xlab="Date", ylab="Total deaths",
xlim=c(xmin,xmax), ylim=c(0,ymax), col='blue',xaxt="n")
text(x=xtick, y=-100, labels = format(xtick,"%b-%d"), srt = 0, xpd = TRUE)
points(as.Date(cases_rv[,1]),cases_rv[,3],pch=19,col='red')
### Predictions tab
par(mfrow=c(1,2))
### Cases at baseline and intervention
ymax<-max(c(cases_rv[,2],rowSums(simul_baseline$daily_incidence),rowSums(simul_interventions$daily_incidence)),na.rm=T)
plot(time,rowSums(simul_baseline$daily_incidence),type='l',lwd=3,col='blue',
main="Baseline", xlab="Date", ylab="New cases per day",ylim=c(0,ymax))
points(as.Date(cases_rv[,1]),cases_rv[,2],pch=19,col='red')
plot(time,rowSums(simul_interventions$daily_incidence),type='l',lwd=3,col='blue',
main="Intervention", xlab="Date", ylab="New cases per day",ylim=c(0,ymax))
points(as.Date(cases_rv[,1]),cases_rv[,2],pch=19,col='red')
# # # Hospital prevalences stratified by H,ICU and Vent
ymax<-max(c((simul_baseline$hospital_surge_beds+simul_baseline$icu_beds+simul_baseline$ventilators),(simul_interventions$hospital_surge_beds+simul_interventions$icu_beds+simul_interventions$ventilators)))
time<-as.Date(out0$mean[,1]+startdate)
coul=c("#047883", "#24A9E2","#051A46")
DM<-as.data.frame(cbind(time,simul_baseline$hospital_surge_beds,simul_baseline$icu_beds,simul_baseline$ventilators))
colnames(DM)<-c("Time","Hospital surge beds","ICU beds","Ventilators")
DM$Time<-as.Date(DM$Time,origin = "1970-01-01")
DMF<-melt(DM, id.vars="Time",measure.vars = c("Hospital surge beds","ICU beds","Ventilators"))
d0<-ggplot(DMF, aes(x = Time, y = value,fill=variable)) +
geom_area()+
scale_fill_manual(values=coul)
DM<-as.data.frame(cbind(time,simul_interventions$hospital_surge_beds,simul_interventions$icu_beds,simul_interventions$ventilators))
colnames(DM)<-c("Time","Hospital surge beds","ICU beds","Ventilators")
DM$Time<-as.Date(DM$Time, origin = "1970-01-01")
DMF<-melt(DM, id.vars="Time",measure.vars = c("Hospital surge beds","ICU beds","Ventilators"))
d1<-ggplot(DMF, aes(x = Time, y = value,fill=variable)) +
geom_area()+
scale_fill_manual(values=coul)
grid.arrange(d0+ylab("Number of Patients")+
ggtitle("Baseline")+
ylim(0, ymax)+
geom_hline(yintercept=(parameters["beds_available"]+parameters["icu_beds_available"]+parameters["ventilators_available"]), linetype="dashed", color = "#047883")+
geom_hline(yintercept=(parameters["icu_beds_available"]+parameters["ventilators_available"]), linetype="dashed", color = "#24A9E2")+
geom_hline(yintercept=parameters["ventilators_available"], linetype="dashed", color = "#051A46")+
theme_bw(),
d1+ylab("Number of Patients")+
ggtitle("Intervention")+
ylim(0, ymax)+
geom_hline(yintercept=(parameters["beds_available"]+parameters["icu_beds_available"]+parameters["ventilators_available"]), linetype="dashed", color = "#047883")+
geom_hline(yintercept=(parameters["icu_beds_available"]+parameters["ventilators_available"]), linetype="dashed", color = "#24A9E2")+
geom_hline(yintercept=parameters["ventilators_available"], linetype="dashed", color = "#051A46")+
theme_bw(),
nrow = 1)
# # # Cumulative mortality at baseline and intervention stratified by hospital status
ymax<-max(rowSums(cbind(simul_baseline$death_treated_hospital,
simul_baseline$death_treated_icu,
simul_baseline$death_treated_ventilator,
simul_baseline$death_untreated_hospital,
simul_baseline$death_untreated_icu,
simul_baseline$death_untreated_ventilator)), rowSums(cbind(simul_interventions$death_treated_hospital,
simul_interventions$death_treated_icu,
simul_interventions$death_treated_ventilator,
simul_interventions$death_untreated_hospital,
simul_interventions$death_untreated_icu,
simul_interventions$death_untreated_ventilator)))
time<-as.Date(out$mean[,1]+startdate)
coul=c("#047883", "#24A9E2","#051A46","#E68029", "#D63304","#D1D604")
DM0<-as.data.frame(cbind(time,
simul_baseline$death_treated_hospital,
simul_baseline$death_treated_icu,
simul_baseline$death_treated_ventilator,
simul_baseline$death_untreated_hospital,
simul_baseline$death_untreated_icu,
simul_baseline$death_untreated_ventilator))
colnames(DM0)<-c("Time", "Treated: Hospital","Treated: ICU","Treated: Ventilator","Untreated: Hospital","Untreated: ICU","Untreated: Ventilator")
DM0$Time<-as.Date(DM0$Time, origin = "1970-01-01")
DMF0<-melt(DM0, id.vars="Time",measure.vars = c("Treated: Hospital","Treated: ICU","Treated: Ventilator","Untreated: Hospital","Untreated: ICU","Untreated: Ventilator"))
m0<-ggplot(DMF0, aes(x = Time, y = value,fill=variable)) +
geom_area()
DM<-as.data.frame(cbind(time,
simul_interventions$death_treated_hospital,
simul_interventions$death_treated_icu,
simul_interventions$death_treated_ventilator,
simul_interventions$death_untreated_hospital,
simul_interventions$death_untreated_icu,
simul_interventions$death_untreated_ventilator))
colnames(DM)<-c("Time","Treated: Hospital","Treated: ICU","Treated: Ventilator","Untreated: Hospital","Untreated: ICU","Untreated: Ventilator")
DM$Time<-as.Date(DM$Time, origin = "1970-01-01")
DMF<-melt(DM, id.vars="Time",measure.vars = c("Treated: Hospital","Treated: ICU","Treated: Ventilator","Untreated: Hospital","Untreated: ICU","Untreated: Ventilator"))
m1<-ggplot(DMF, aes(x = Time, y = value,fill=variable)) +
geom_area()
grid.arrange(m0+ylab("Cumulatice mortality")+
ggtitle("Baseline")+
ylim(0, ymax),
m1+ylab("Cumulatice mortality")+
ggtitle("Intervention")+
ylim(0, ymax),
nrow = 1)
# Estimated basic reproduction number, R_t
par(mfrow=c(1,2))
ymax<-max(c(simul_baseline$Rt[!is.na(simul_baseline$Rt)],simul_interventions$Rt[!is.na(simul_interventions$Rt)]))
plot(time,simul_baseline$Rt,type='l',lwd=3,col='black',
main="Baseline", xlab="Date", ylab="Reproduction number",ylim=c(0,ymax))
lines(time,simul_baseline$Rt/simul_baseline$Rt,lwd=2,col='grey')
plot(time,simul_interventions$Rt,type='l',lwd=3,col='black',
main="Intervention", xlab="Date", ylab="Reproduction number",ylim=c(0,ymax))
lines(time,simul_interventions$Rt/simul_interventions$Rt,lwd=2,col='grey')
## AGE DEPENDENT MORTALITY
cinc_mort_H1 <- parameters["nus"]*parameters["pdeath_h"]*(out$mean[,(Hindex+1)])
cinc_mort_HC1 <- parameters["nusc"]*parameters["pdeath_hc"]*(out$mean[,(HCindex+1)])
cinc_mort_ICU1 <- parameters["nu_icu"]*parameters["pdeath_icu"]*out$mean[,(ICUindex+1)]
cinc_mort_ICUC1 <- parameters["nu_icuc"]*parameters["pdeath_icuc"]*out$mean[,(ICUCindex+1)]
cinc_mort_Vent1 <- parameters["nu_vent"]*parameters["pdeath_vent"]*out$mean[,(Ventindex+1)]
cinc_mort_VentC1 <- parameters["nu_ventc"]*parameters["pdeath_ventc"]*out$mean[,(VentCindex+1)]
totage1<-as.data.frame(cinc_mort_H1+cinc_mort_HC1+cinc_mort_ICU1+cinc_mort_ICUC1+cinc_mort_Vent1+cinc_mort_VentC1)
basemort_H1<-(out$mean[,(Hindex+1)])
basemort_HC1<-(out$mean[,(HCindex+1)])
basemort_ICU1<-(out$mean[,(ICUindex+1)])
basemort_ICUC1<-(out$mean[,(ICUCindex+1)])
basemort_Vent1<-(out$mean[,(Ventindex+1)])
basemort_VentC1<-(out$mean[,(VentCindex+1)])
totbase1<-as.data.frame(basemort_H1+basemort_HC1+basemort_ICU1+basemort_ICUC1+basemort_Vent1+basemort_VentC1)
tc<-c()
ages<-seq(0,100,by=5)
for (i in 1:dim(cinc_mort_H1)[1]) {
for (j in 1:dim(cinc_mort_H1)[2]) {
tc<-rbind(tc,c(i,ages[j],totage1[i,j]*ifr[j,2]+totbase1[i,j]*mort[j]))
}
}
tc<-as.data.frame(tc)
colnames(tc)<-c("Day","Age","value")
tc$Age<-as.factor(tc$Age)
p6<-ggplot(data=tc, aes(x=Day,y=value,fill=Age))+
geom_bar(stat = "identity",position="fill", width=1)+
ylab("Proportion of deaths")
inc_mort_H0 <- parameters["nus"]*parameters["pdeath_h"]*(out0$mean[,(Hindex+1)])
inc_mort_HC0 <- parameters["nusc"]*parameters["pdeath_hc"]*(out0$mean[,(HCindex+1)])
inc_mort_ICU0 <- parameters["nu_icu"]*parameters["pdeath_icu"]*out0$mean[,(ICUindex+1)]
inc_mort_ICUC0 <- parameters["nu_icuc"]*parameters["pdeath_icuc"]*out0$mean[,(ICUCindex+1)]
inc_mort_Vent0 <- parameters["nu_vent"]*parameters["pdeath_vent"]*out0$mean[,(Ventindex+1)]
inc_mort_VentC0 <- parameters["nu_ventc"]*parameters["pdeath_ventc"]*out0$mean[,(VentCindex+1)]
totage0<-as.data.frame(inc_mort_H0+inc_mort_HC0+inc_mort_ICU0+inc_mort_ICUC0+inc_mort_Vent0+inc_mort_VentC0)
basemort_H0<-(out0$mean[,(Hindex+1)])
basemort_HC0<-(out0$mean[,(HCindex+1)])
basemort_ICU0<-(out0$mean[,(ICUindex+1)])
basemort_ICUC0<-(out0$mean[,(ICUCindex+1)])
basemort_Vent0<-(out0$mean[,(Ventindex+1)])
basemort_VentC0<-(out0$mean[,(VentCindex+1)])
totbase0<-as.data.frame(basemort_H0+basemort_HC0+basemort_ICU0+basemort_ICUC0+basemort_Vent0+basemort_VentC0)
tc0<-c()
for (i in 1:dim(cinc_mort_H1)[1]) {
for (j in 1:dim(cinc_mort_H1)[2]) {
tc0<-rbind(tc0,c(i,ages[j],totage0[i,j]*ifr[j,2]+totbase0[i,j]*mort[j]))
}
}
tc0<-as.data.frame(tc0)
colnames(tc0)<-c("Day","Age","value")
tc0$Age<-as.factor(tc0$Age)
p16<-ggplot(data=tc0, aes(x=Day,y=value,fill=Age))+
geom_bar(stat = "identity",position="fill", width=1)+ylab("Proportion of deaths")
grid.arrange(p16+theme_minimal(),
p6+theme_minimal(),
nrow=1)
####### PLOT Abs
samp.sizes<-round(rnorm(length(times),parameters["sample_size"],parameters["sample_size"]/5))
ab0<-as.data.frame(matrix(0,nrow=length(times)*1000,ncol=2))
colnames(ab0)<-c("Time","Ab")
ab0$Time<-rep(times,1000)
aux<-c()
for (i in 1:1000) {
num.inf.samp <- rbinom(length(times), size = samp.sizes, prob = (simul_baseline$ab_all_ages/simul_baseline$N))
aux<-c(aux,num.inf.samp/samp.sizes)
}
ab0$Ab<-aux
ggplot(ab0,aes(x=Time,y=Ab,group=Time))+geom_boxplot()
samp.sizes<-round(rnorm(length(times),parameters["sample_size"],parameters["sample_size"]/5))
ab<-as.data.frame(matrix(0,nrow=length(times)*1000,ncol=2))
colnames(ab)<-c("Time","Ab")
ab$Time<-rep(times,1000)
aux<-c()
for (i in 1:1000) {
num.inf.samp <- rbinom(length(times), size = samp.sizes, prob = (simul_interventions$ab_all_ages/simul_interventions$N))
aux<-c(aux,num.inf.samp/samp.sizes)
}
se<-0.5
sp<-0.9
ab$Ab<-se*aux+(1-sp)*(1-aux)
ggplot(ab,aes(x=Time,y=Ab,group=Time))+geom_boxplot()
##########################################################################################################################
###### SUMMARY METRICS ################################################################################################
########################################################################################################################
infected0<-tail((rowSums(out0$mean[,(Rindex+1)])),1)/sum(popstruc[,2])
infected0
infected1<-tail((rowSums(out$mean[,(Rindex+1)])),1)/sum(popstruc[,2])
infected1
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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.