surveillance (version 1.12.1)

bodaDelay: Bayesian Outbreak Detection in the Presence of Reporting Delays

Description

The function takes range values of the surveillance time series sts and for each time point uses a Bayesian model of the negative binomial family with log link inspired by the work of Noufaily et al. (2012) and of Manitz and H�hle{Hoehle} (2014). It allows delay-corrected aberration detection as explained in Salmon et al. (2015). A reportingTriangle has to be provided in the control slot.

Usage

bodaDelay(sts, control = list(
  range = NULL, b = 3, w = 3, mc.munu = 100, mc.y = 10,
  pastAberrations = TRUE, verbose = FALSE,
  alpha = 0.01, trend = TRUE, limit54 = c(5,4), 
  inferenceMethod = c("asym","INLA"), quantileMethod = c("MC","MM"),
  noPeriods = 1, pastWeeksNotIncluded = 26, delay = TRUE))

Arguments

sts

sts-object to be analysed. Needs to have a reporting triangle.

control

list of control arguments: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[ob

References

Farrington, C.P., Andrews, N.J, Beale A.D. and Catchpole, M.A. (1996): A statistical algorithm for the early detection of outbreaks of infectious disease. J. R. Statist. Soc. A, 159, 547-563. Noufaily, A., Enki, D.G., Farrington, C.P., Garthwaite, P., Andrews, N.J., Charlett, A. (2012): An improved algorithm for outbreak detection in multiple surveillance systems. Statistics in Medicine, 32 (7), 1206-1222. Salmon, M., Schumacher, D., Stark, K., H�hle{Hoehle}, M. (2015): Bayesian outbreak detection in the presence of reporting delays. Biometrical Journal, 57 (6), 1051-1067.

Examples

data("stsNewport")
salm.Normal <- list()
salmDelayAsym <- list()
for (week in 43:45){
  listWeeks <- as.Date(row.names(stsNewport@control$reportingTriangle$n), origin="1970-01-01")
  dateObs <- listWeeks[isoWeekYear(listWeeks)$ISOYear==2011 &
                       isoWeekYear(listWeeks)$ISOWeek==week]
  stsC <- sts_observation(stsNewport,
                          dateObservation=dateObs,
                          cut=TRUE)
  inWeeks <- which(formatDate(epoch(stsC), "%G") == 2011 & 
                     isoWeekYear(as.Date(epoch(stsC)))$ISOWeek>=40 & 
                     isoWeekYear(as.Date(epoch(stsC)))$ISOWeek<=48)
  
  rangeTest <- inWeeks
  alpha <- 0.07

  # Control slot for Noufaily method          
  controlNoufaily <- list(range=rangeTest,noPeriods=10,
                          b=4,w=3,weightsThreshold=2.58,pastWeeksNotIncluded=26,
                          pThresholdTrend=1,thresholdMethod="nbPlugin",alpha=alpha*2,
                          limit54=c(0,50))
  
  # Control slot for the Proposed algorithm with D=0 correction
  controlNormal <- list(range = rangeTest, b = 4, w = 3,
                        reweight = TRUE, mc.munu=10000, mc.y=100,
                        verbose = FALSE,
                        alpha = alpha, trend = TRUE,
                        limit54=c(0,50), 
                        noPeriods = 10, pastWeeksNotIncluded = 26,
                        delay=FALSE)
  
  # Control slot for the Proposed algorithm with D=10 correction
  controlDelayNorm <-  list(range = rangeTest, b = 4, w = 3,
                            reweight = FALSE, mc.munu=10000, mc.y=100,
                            verbose = FALSE,
                            alpha = alpha, trend = TRUE,
                            limit54=c(0,50), 
                            noPeriods = 10, pastWeeksNotIncluded = 26,
                            delay=TRUE,inferenceMethod="asym")
  
  set.seed(1)
  salm.Normal[[week]] <- farringtonFlexible(stsC, controlNoufaily)
  salmDelayAsym[[week]] <- bodaDelay(stsC, controlDelayNorm)
}

opar <- par(mfrow=c(2,3))
lapply(salmDelayAsym[c(43,44,45)],plot, legend=NULL, main="", ylim=c(0,35))
lapply(salm.Normal[c(43,44,45)],plot, legend=NULL, main="", ylim=c(0,35))
par(opar)