The function takes
range values of a univariate surveillance time
sts and for each time point uses a negative binomial
regression model to compute the predictive posterior distribution for
the current observation. The
quantile of this predictive distribution is then
used as bound: If the actual observation is above the bound an alarm
The Bayesian Outbreak Detection Algorithm (
boda) is due to
Manitz and Höhle (2013) and its implementation is
illustrated in Salmon et al. (2016).
boda should be considered as an experiment, see the
Warning section below!
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object of class sts (including the
Control object given as a
This function is currently experimental!! It also heavily depends on the INLA package so changes there might affect the operational ability of this function. Since the computations for the Bayesian GAM are quite involved do not expect this function to be particularly fast. Future work could focus on improving the speed, e.g., one issue would be to make the inference work in a sequential fashion.
This function requires the R package INLA, which is currently
not available from CRAN. It can be obtained from INLA's own
J. Manitz, M. Höhle, M. Salmon
Manitz, J. and Höhle, M. (2013): Bayesian outbreak detection algorithm for monitoring reported cases of campylobacteriosis in Germany. Biometrical Journal, 55(4), 509-526.
Salmon, M., Schumacher, D. and Höhle, M. (2016): Monitoring count time series in R: Aberration detection in public health surveillance. Journal of Statistical Software, 70 (10), 1-35. doi: 10.18637/jss.v070.i10
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## Not run: ## running this example takes a couple of minutes #Load the campylobacteriosis data for Germany data("campyDE") #Make an sts object from the data.frame cam.sts <- sts(epoch=campyDE$date, observed=campyDE$case, state=campyDE$state) #Define monitoring period # range <- which(epoch(cam.sts)>=as.Date("2007-01-01")) # range <- which(epoch(cam.sts)>=as.Date("2011-12-10")) range <- tail(1:nrow(cam.sts),n=2) control <- list(range=range, X=NULL, trend=TRUE, season=TRUE, prior='iid', alpha=0.025, mc.munu=100, mc.y=10, samplingMethod = "joint") #Apply the boda algorithm in its simples form, i.e. spline is #described by iid random effects and no extra covariates library("INLA") # needs to be attached cam.boda1 <- boda(cam.sts, control=control) plot(cam.boda1, xlab='time [weeks]', ylab='No. reported', dx.upperbound=0) ## End(Not run)
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