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base.r
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library(rstan)
library(data.table)
library(lubridate)
perl <- "D:/strawberry/perl/bin/perl.exe" #apontar para o perl interperter (http://strawberryperl.com/)
library(gdata)
library(EnvStats)
countries <- c(
"Portugal",
"Italy",
"Germany",
"Spain",
"United_Kingdom",
"France",
"Norway",
"Belgium",
"Austria",
"Sweden",
"Switzerland"
)
args = commandArgs(trailingOnly=TRUE)
if(length(args) == 0) {
args = 'base'
}
StanModel = args[1]
print(sprintf("Running %s",StanModel))
## Reading all data
d=readRDS('data/COVID-19-up-to-date.rds')
## acrescenta dados extra para PT
d_pt<-read.csv("data/extra_data_PT.csv")
d_pt$t<-lubridate::decimal_date(as.Date(d_pt$ï..dateRep, format = "%d/%m/%Y"))
names(d_pt)<-names(d)
d<-rbind(d,d_pt)
d <- d[order(d$Countries, d$t, decreasing = FALSE), ]
## get IFR
ifr.by.country = read.csv("data/weighted_fatality.csv")
ifr.by.country$country = as.character(ifr.by.country[,2])
ifr.by.country$country[ifr.by.country$country == "United Kingdom"] = "United_Kingdom"
serial.interval = read.csv("data/serial_interval.csv")
covariates = read.csv('data/interventions.csv', stringsAsFactors = FALSE)
covariates <- covariates[1:11, c(1,2,3,4,5,6, 7, 8)]
## making all covariates that happen after lockdown to have same date as lockdown
covariates$schools_universities[covariates$schools_universities > covariates$lockdown] <- covariates$lockdown[covariates$schools_universities > covariates$lockdown]
covariates$travel_restrictions[covariates$travel_restrictions > covariates$lockdown] <- covariates$lockdown[covariates$travel_restrictions > covariates$lockdown]
covariates$public_events[covariates$public_events > covariates$lockdown] <- covariates$lockdown[covariates$public_events > covariates$lockdown]
covariates$sport[covariates$sport > covariates$lockdown] <- covariates$lockdown[covariates$sport > covariates$lockdown]
covariates$social_distancing_encouraged[covariates$social_distancing_encouraged > covariates$lockdown] <- covariates$lockdown[covariates$social_distancing_encouraged > covariates$lockdown]
covariates$self_isolating_if_ill[covariates$self_isolating_if_ill > covariates$lockdown] <- covariates$lockdown[covariates$self_isolating_if_ill > covariates$lockdown]
p <- ncol(covariates) - 1
forecast = 0
DEBUG = FALSE
if(DEBUG == FALSE) {
N2 = 90 # Increase this for a further forecast (tem que se aumentar à medida que se tem dados novos)
countries=c("Portugal","Spain","Sweden") #Comentar para fazer para todos os países
} else {
### For faster runs:
# countries = c("Austria","Belgium") #,Spain")
N2 = 75
}
# countries = c("Italy","United_Kingdom","Spain","Norway","Austria","Switzerland")
dates = list()
reported_cases = list()
stan_data = list(M=length(countries),N=NULL,p=p,x1=poly(1:N2,2)[,1],x2=poly(1:N2,2)[,2],
y=NULL,covariate1=NULL,covariate2=NULL,covariate3=NULL,covariate4=NULL,covariate5=NULL,covariate6=NULL,covariate7=NULL,deaths=NULL,f=NULL,
N0=6,cases=NULL,LENGTHSCALE=7,SI=serial.interval$fit[1:N2],
EpidemicStart = NULL) # N0 = 6 to make it consistent with Rayleigh
deaths_by_country = list()
for(Country in countries) {
IFR=ifr.by.country$weighted_fatality[ifr.by.country$country == Country]
covariates1 <- covariates[covariates$Country == Country, 2:8]
d1=d[d$Countries.and.territories==Country,]
d1$date = as.Date(d1$DateRep,format='%d/%m/%Y')
d1$t = decimal_date(d1$date)
d1=d1[order(d1$t),]
index = which(d1$Cases>0)[1]
index1 = which(cumsum(d1$Deaths)>=10)[1] # also 5
index2 = index1-30
print(sprintf("First non-zero cases is on day %d, and 30 days before 5 days is day %d",index,index2))
d1=d1[index2:nrow(d1),]
stan_data$EpidemicStart = c(stan_data$EpidemicStart,index1+1-index2)
for (ii in 1:ncol(covariates1)) {
covariate = names(covariates1)[ii]
d1[covariate] <- (as.Date(d1$DateRep, format='%d/%m/%Y') >= as.Date(covariates1[1,covariate]))*1 # should this be > or >=?
}
dates[[Country]] = d1$date
# hazard estimation
N = length(d1$Cases)
print(sprintf("%s has %d days of data",Country,N))
forecast = N2 - N
if(forecast < 0) {
print(sprintf("%s: %d", Country, N))
print("ERROR!!!! increasing N2")
N2 = N
forecast = N2 - N
}
h = rep(0,forecast+N) # discrete hazard rate from time t = 1, ..., 100
if(DEBUG) { # OLD -- but faster for testing this part of the code
mean = 18.8
cv = 0.45
for(i in 1:length(h))
h[i] = (IFR*pgammaAlt(i,mean = mean,cv=cv) - IFR*pgammaAlt(i-1,mean = mean,cv=cv)) / (1-IFR*pgammaAlt(i-1,mean = mean,cv=cv))
} else { # NEW
mean1 = 5.1; cv1 = 0.86; # infection to onset
mean2 = 18.8; cv2 = 0.45 # onset to death
## assume that IFR is probability of dying given infection
x1 = rgammaAlt(5e6,mean1,cv1) # infection-to-onset ----> do all people who are infected get to onset?
x2 = rgammaAlt(5e6,mean2,cv2) # onset-to-death
f = ecdf(x1+x2)
convolution = function(u) (IFR * f(u))
h[1] = (convolution(1.5) - convolution(0))
for(i in 2:length(h)) {
h[i] = (convolution(i+.5) - convolution(i-.5)) / (1-convolution(i-.5))
}
}
s = rep(0,N2)
s[1] = 1
for(i in 2:N2) {
s[i] = s[i-1]*(1-h[i-1])
}
f = s * h
y=c(as.vector(as.numeric(d1$Cases)),rep(-1,forecast))
reported_cases[[Country]] = as.vector(as.numeric(d1$Cases))
deaths=c(as.vector(as.numeric(d1$Deaths)),rep(-1,forecast))
cases=c(as.vector(as.numeric(d1$Cases)),rep(-1,forecast))
deaths_by_country[[Country]] = as.vector(as.numeric(d1$Deaths))
covariates2 <- as.data.frame(d1[, colnames(covariates1)])
# x=1:(N+forecast)
covariates2[N:(N+forecast),] <- covariates2[N,]
## append data
stan_data$N = c(stan_data$N,N)
stan_data$y = c(stan_data$y,y[1]) # just the index case!
# stan_data$x = cbind(stan_data$x,x)
stan_data$covariate1 = cbind(stan_data$covariate1,covariates2[,1])
stan_data$covariate2 = cbind(stan_data$covariate2,covariates2[,2])
stan_data$covariate3 = cbind(stan_data$covariate3,covariates2[,3])
stan_data$covariate4 = cbind(stan_data$covariate4,covariates2[,4])
stan_data$covariate5 = cbind(stan_data$covariate5,covariates2[,5])
stan_data$covariate6 = cbind(stan_data$covariate6,covariates2[,6])
stan_data$covariate7 = cbind(stan_data$covariate7,covariates2[,7])
stan_data$f = cbind(stan_data$f,f)
stan_data$deaths = cbind(stan_data$deaths,deaths)
stan_data$cases = cbind(stan_data$cases,cases)
stan_data$N2=N2
stan_data$x=1:N2
if(length(stan_data$N) == 1) {
stan_data$N = as.array(stan_data$N)
}
}
stan_data$covariate2 = 0 * stan_data$covariate2 # remove travel bans
stan_data$covariate4 = 0 * stan_data$covariate5 # remove sport
#stan_data$covariate1 = stan_data$covariate1 # school closure
stan_data$covariate2 = stan_data$covariate7 # self-isolating if ill
#stan_data$covariate3 = stan_data$covariate3 # public events
# create the `any intervention` covariate
stan_data$covariate4 = 1*as.data.frame((stan_data$covariate1+
stan_data$covariate3+
stan_data$covariate5+
stan_data$covariate6+
stan_data$covariate7) >= 1)
stan_data$covariate5 = stan_data$covariate5 # lockdown
stan_data$covariate6 = stan_data$covariate6 # social distancing encouraged
stan_data$covariate7 = 0 # models should only take 6 covariates
if(DEBUG) {
for(i in 1:length(countries)) {
write.csv(
data.frame(date=dates[[i]],
`school closure`=stan_data$covariate1[1:stan_data$N[i],i],
`self isolating if ill`=stan_data$covariate2[1:stan_data$N[i],i],
`public events`=stan_data$covariate3[1:stan_data$N[i],i],
`government makes any intervention`=stan_data$covariate4[1:stan_data$N[i],i],
`lockdown`=stan_data$covariate5[1:stan_data$N[i],i],
`social distancing encouraged`=stan_data$covariate6[1:stan_data$N[i],i]),
file=sprintf("results/%s-check-dates.csv",countries[i]),row.names=F)
}
}
stan_data$y = t(stan_data$y)
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)
m = stan_model(paste0('stan-models/',StanModel,'.stan'))
if(DEBUG) {
fit = sampling(m,data=stan_data,iter=40,warmup=20,chains=2)
} else {
# fit = sampling(m,data=stan_data,iter=4000,warmup=2000,chains=8,thin=4,control = list(adapt_delta = 0.90, max_treedepth = 10))
fit = sampling(m,data=stan_data,iter=200,warmup=100,chains=4,thin=4,control = list(adapt_delta = 0.90, max_treedepth = 10))
}
out = rstan::extract(fit)
prediction = out$prediction
estimated.deaths = out$E_deaths
estimated.deaths.cf = out$E_deaths0
JOBID = Sys.getenv("PBS_JOBID")
if(JOBID == "")
JOBID = as.character(abs(round(rnorm(1) * 1000000)))
print(sprintf("Jobid = %s",JOBID))
save.image(paste0('results/',StanModel,'-',JOBID,'.Rdata'))
save(fit,prediction,dates,reported_cases,deaths_by_country,countries,estimated.deaths,estimated.deaths.cf,out,covariates,file=paste0('results/',StanModel,'-',JOBID,'-stanfit.Rdata'))
# to visualize results
library(bayesplot)
filename <- paste0('base-',JOBID)
plot_labels <- c("School Closure",
"Self Isolation",
"Public Events",
"First Intervention",
"Lockdown", 'Social distancing')
alpha = (as.matrix(out$alpha))
colnames(alpha) = plot_labels
g = (mcmc_intervals(alpha, prob = .9))
ggsave(sprintf("results/%s-covars-alpha-log.pdf",filename),g,width=4,height=6)
g = (mcmc_intervals(alpha, prob = .9,transformations = function(x) exp(-x)))
ggsave(sprintf("results/%s-covars-alpha.pdf",filename),g,width=4,height=6)
mu = (as.matrix(out$mu))
colnames(mu) = countries
g = (mcmc_intervals(mu,prob = .9))
ggsave(sprintf("results/%s-covars-mu.pdf",filename),g,width=4,height=6)
dimensions <- dim(out$Rt)
Rt = (as.matrix(out$Rt[,dimensions[2],]))
colnames(Rt) = countries
g = (mcmc_intervals(Rt,prob = .9))
ggsave(sprintf("results/%s-covars-final-rt.pdf",filename),g,width=4,height=6)
system(paste0("Rscript plot-3-panel.r ", filename,'.Rdata'))
system(paste0("Rscript plot-forecast.r ",filename,'.Rdata')) ## to run this code you will need to adjust manual values of forecast required