surveillance (version 1.11.0)

nowcast: Adjust a univariate time series of counts for observed but-not-yet-reported events

Description

Nowcasting can help to obtain up-to-date information on trends during a situation where reports about events arrive with delay. For example in public health reporting, reports about important indicators (such as occurrence of cases) are prone to be delayed due to for example manual quality checking and reporting system hierarchies. Altogether, the delays are subject to a delay distribution, which may or may not vary over time.

Usage

nowcast(now,when,data,dEventCol="dHospital",dReportCol="dReport",
        method=c("bayes.notrunc","bayes.notrunc.bnb","lawless","bayes.trunc",
                 "unif","bayes.trunc.ddcp"),
        aggregate.by="1 day",
        D=15, m=NULL,
        control=list(
            dRange=NULL,alpha=0.05,nSamples=1e3,
            N.tInf.prior=c("poisgamma","pois","unif"),
            N.tInf.max=300, gd.prior.kappa=0.1,
            ddcp=list(ddChangepoint=NULL,
                logLambda=c("iidLogGa","tps","rw1","rw2"),
                tau.gamma=1,eta.mu=NULL, eta.prec=NULL,
                mcmc=c(burnin=2500,sample=10000,thin=1)),
            score=FALSE,predPMF=FALSE))

Arguments

now

an object of class Date denoting the day at which to do the nowcast. This corresponds to $T$ in the notation of H�hle{Hoehle} and an der Heiden (2014).

when

a vector of Date objects denoting the day(s) for which the projections are to be done. One needs to ensure that each element in when is smaller or equal to now.

data

A data frame with one row per case -- for each case on needs information on the day of the event (e.g. hospitalization) and the day of report of this event.

dEventCol

The name of the column in data which contains the date of the event, e.g. hospitalization. Default: "dHospital".

dReportCol

Name of the column in data containing the date at which the report arrives at the respective register. Default: "dReport".

method

A vector of strings denoting the different methods for doing the nowcasting. Note that results of the first name in this list are officially returned by the function. However, it is possible to specify several methods here, e.g., in order

aggregate.by

Time scale used for the temporal aggregation of the records in the data data. See linelist2sts and seq.Date for further informat

D

Maximum possible or maximum relevant delay (unit: aggregate.by). Default: 15.

m

Moving window for the estimation of the delay distribution. Default: NULL, i.e. take all values at all times.

control

A list with named arguments controlling the functionality of the nowcasting. [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Value

• nowcast returns an object of "stsNC". The upperbound slot contains the median of the method specified at the first position the argument method. The slot pi (for prediction interval) contains the equal tailed (1-$\alpha$)*100% prediction intervals, which are calculated based on the predictive distributions in slot predPMF. Furthermore, slot truth contains an sts object containing the true number of cases (if possible to compute it based on the data in data. Finally, slot SR contains the results for the proper scoring rules (requires truth to be calculable).

encoding

latin1

Details

The methodological details of the nowcasting procedures are described in H�hle{Hoehle} M and an der Heiden M (2014).

References

H�hle{Hoehle} M and an der Heiden M (2014), Bayesian Nowcasting during the STEC O104:H4 Outbreak in Germany, 2011, Biometrics, 70(4):993-1002. http://dx.doi.org/10.1111/biom.12194. A preprint is available as http://people.su.se/~mhh/pubs/hoehle_anderheiden2014-preprint.pdf.

Examples

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))

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))))

###Using runjags to do Bayesian model with changepoint(s) -- this might take
###a while.
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