algo.farrington.Rd
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.
algo.farrington(disProgObj, control=list(
range=NULL, b=5, w=3, reweight=TRUE, verbose=FALSE, plot=FALSE,
alpha=0.05, trend=TRUE, limit54=c(5,4), powertrans="2/3",
fitFun="algo.farrington.fitGLM.fast"))
object of class disProgObj (including the observed
and the
state
time series.)
list of control parameters
range
Specifies the index of all timepoints which
should be tested. If range is NULL
the maximum number
of possible weeks is used (i.e. as many weeks as possible while
still having enough reference values).
b
how many years back in time to include when forming the base counts.
w
windows size, i.e. number of weeks to include before and after the current week
reweight
Boolean specifying whether to perform reweight step
trend
If TRUE
a trend is included and kept in
case the conditions documented in Farrington et al. (1996) are met
(see the results). If FALSE
then NO trend is fit.
verbose
Boolean indicating whether to show extra debugging information.
plot
Boolean specifying whether to show the final GLM model fit graphically (use History|Recording to see all pictures).
powertrans
Power transformation to apply to the data. Use either "2/3" for skewness correction (Default), "1/2" for variance stabilizing transformation or "none" for no transformation.
alpha
An approximate (two-sided) \((1-\alpha)\) prediction interval is calculated.
limit54
To avoid alarms in cases where the time series only
has about 0-2 cases the algorithm uses the following heuristic
criterion (see Section 3.8 of the Farrington paper) to protect
against low counts: no alarm is sounded if fewer than
\(cases=5\) reports were received in the past \(period=4\)
weeks. limit54=c(cases,period)
is a vector allowing the
user to change these numbers. Note: As of version 0.9-7 the
term "last" period of weeks includes the current week -
otherwise no alarm is sounded for horrible large numbers if
the four weeks before that are too low.
fitFun
String containing the name of the fit
function to be used for fitting the GLM. The options are
algo.farrington.fitGLM.fast
(default) and
algo.farrington.fitGLM
or
algo.farrington.fitGLM.populationOffset
. See details of
algo.farrington.fitGLM
for more information.
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
algo.farrington.fitGLM
,
algo.farrington.threshold
An improved Farrington algorithm is available as function
farringtonFlexible
.
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.
#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=(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")
if (FALSE) {
#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))
}