algo.farrington {surveillance} | R Documentation |
The function takes range
values of the time series counts
and for each uses a GLM to predict the number of counts according to the procedure by Farrington et. al. This is then compared to the observed number of counts and in case an exceedance of the confidence interval calculated is seen an alarm is raised.
algo.farrington(disProgObj, control=list(range=NULL, b=3, w=3, reweight=TRUE,verbose=FALSE,alpha=0.01))
disProgObj |
object of class disProgObj (including the observed and the state chain) |
control |
Control object
|
The following steps are perfomed according to the Farrington et. al. paper.
An object of class SurvRes
.
M. Höhle
A statistical algorithm for the early detection of outbreaks of infectious disease, Farrington, C.P., Andrews, N.J, Beale A.D. and Catchpole, M.A. (1996). , J. R. Statist. Soc. A, 159, 547-563.
algo.farrington.fitGLM
,algo.farrington.threshold
#Read Salmonella Agona data library(xtable) salmonella.agona <- readData("salmonella.agona",week53to52=FALSE) #Do surveillance for the last 100 weeks. n <- length(salmonella.agona$observed) #Set control parameters. control <- list(b=4,w=3,range=(n-100):n,reweight=TRUE, verbose=FALSE,alpha=0.01) res <- algo.farrington(salmonella.agona,control=control) #Plot the result. plot(res,disease="Salmonella Agona",method="Farrington")