Description Usage Arguments Warning Note Author(s) References Examples
The function takes range
values of a univariate surveillance time
series sts
and for each time point uses a negative binomial
regression model to compute the predictive posterior distribution for
the current observation. The
(1alpha)*100%
quantile of this predictive distribution is then
used as bound: If the actual observation is above the bound an alarm
is raised.
The Bayesian Outbreak Detection Algorithm (boda
) is due to
Manitz and Höhle (2013) and its implementation is
illustrated in Salmon et al. (2016).
However, boda
should be considered as an experiment, see the
Warning section below!
1 2 3 4 5 6 7 
sts 
object of class sts (including the 
control 
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
repository via
install.packages("INLA", repos="https://inla.rinladownload.org/R/stable")
.
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), 509526.
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), 135. doi: 10.18637/jss.v070.i10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  ## 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("20070101"))
# range < which(epoch(cam.sts)>=as.Date("20111210"))
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)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.