Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/algo_farrington.R
The function takes range
values of the surveillance time
series disProgObj
and for each time point uses a GLM to
predict the number of counts according to the procedure by
Farrington et al. (1996). This is then compared to the observed
number of counts. If the observation is above a specific quantile of
the prediction interval, then an alarm is raised.
1 2 3 4 5 
disProgObj 
object of class disProgObj (including the 
control 
list of control parameters

The following steps are performed according to the Farrington et al. (1996) paper.
fit of the initial model and initial estimation of mean and overdispersion.
calculation of the weights omega (correction for past outbreaks)
refitting of the model
revised estimation of overdispersion
rescaled model
omission of the trend, if it is not significant
repetition of the whole procedure
calculation of the threshold value
computation of exceedance score
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, 547563.
algo.farrington.fitGLM
,algo.farrington.threshold
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 27  #Read Salmonella Agona data
data("salmonella.agona")
#Do surveillance for the last 100 weeks.
n < length(salmonella.agona$observed)
#Set control parameters.
control < list(b=4,w=3,range=(n100):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")
## Not run:
#Generate Poisson counts and convert into an "sts" object
set.seed(123)
x < rpois(520,lambda=1)
sts < sts(observed=x, state=x*0, freq=52)
#Compare timing of the two possible fitters for algo.farrington (here using S4)
system.time( sts1 < farrington(sts, control=list(range=312:520,
fitFun="algo.farrington.fitGLM.fast")))
system.time( sts2 < farrington(sts, control=list(range=312:520,
fitFun="algo.farrington.fitGLM")))
#Check if results are the same
stopifnot(upperbound(sts1) == upperbound(sts2))
## End(Not run)

Loading required package: sp
Loading required package: xtable
Loading required package: polyCub
This is surveillance 1.14.0. For overview type 'help(surveillance)'.
Running algo.farrington on area 1 out of 1
user system elapsed
1.365 0.016 1.393
Running algo.farrington on area 1 out of 1
user system elapsed
2.345 0.000 2.367
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