data("husO104Hosp")
#Extract the reporting triangle at a specific day
t.repTriangle <- as.Date("2011-07-04")
#Use 'void' nowcasting procedure (we just want the reporting triangle)
nc <- nowcast(now=t.repTriangle,when=t.repTriangle,
dEventCol="dHosp",dReportCol="dReport",data=husO104Hosp,
D=15,method="unif")
#Show reporting triangle
reportingTriangle(nc)
#Perform Bayesian nowcasting assuming the delay distribution is stable over time
nc.control <- list(N.tInf.prior=structure("poisgamma",
mean.lambda=50,var.lambda=3000),
nSamples=1e2)
t.repTriangle <- as.Date("2011-06-10")
when <- seq(t.repTriangle-3,length.out=10,by="-1 day")
nc <- nowcast(now=t.repTriangle,when=when,
dEventCol="dHosp",dReportCol="dReport",data=husO104Hosp,
D=15,method="bayes.trunc",control=nc.control)
#Show time series and posterior median forecast/nowcast
plot(nc,xaxis.tickFreq=list("%d"=atChange,"%m"=atChange),
xaxis.labelFreq=list("%d"=at2ndChange),xaxis.labelFormat="%d-%b",
xlab="Time (days)",lty=c(1,1,1,1),lwd=c(1,1,2))
if (FALSE) {
### Using runjags to do a Bayesian model with changepoint(s)
### -- this might take a while
nc.control.ddcp <- modifyList(nc.control,
list(gd.prior.kappa=0.1,
ddcp=list(ddChangepoint=as.Date(c("2011-05-23")),
logLambda="tps",
tau.gamma=1,
mcmc=c(burnin=1000,sample=1000,thin=1,
adapt=1000,store.samples=FALSE))))
nc.ddcp <- nowcast(now=t.repTriangle,when=when,
dEventCol="dHosp",dReportCol="dReport",
data=husO104Hosp, aggregate.by="1 day",
method="bayes.trunc.ddcp", D=15,
control=nc.control.ddcp)
plot(nc.ddcp,legend.opts=NULL,
xaxis.tickFreq=list("%d"=atChange,"%m"=atChange),
xaxis.labelFreq=list("%d"=at2ndChange),xaxis.labelFormat="%d-%b",
xlab="Time (days)",lty=c(1,1,1,1),lwd=c(1,1,2))
lambda <- attr(delayCDF(nc.ddcp)[["bayes.trunc.ddcp"]],"model")$lambda
showIdx <- seq(which( max(when) == epoch(nc.ddcp))) #seq(ncol(lambda))
matlines( showIdx,t(lambda)[showIdx,],col="gray",lwd=c(1,2,1),lty=c(2,1,2))
legend(x="topright",c(expression(lambda(t)),"95% CI"),col="gray",lwd=c(2,1),lty=c(1,2))
}
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