surveillance: algo.farrington – R documentation

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algo.farrington

Surveillance for Count Time Series Using the Classic Farrington Method

Description

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.

Usage

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"))

Arguments

disProgObj

object of class disProgObj (including the observed and the state time series.)

control

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-α) 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.

Details

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

Value

An object of class "survRes".

Author(s)

M. Höhle

References

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.

Examples

#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")

## 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)

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

v1.19.1

GPL-2

Authors

Michael H<f6>hle [aut, ths] (<https://orcid.org/0000-0002-0423-6702>), Sebastian Meyer [aut, cre] (<https://orcid.org/0000-0002-1791-9449>), Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Saban<e9>s Bov<e9> [ctb], Ma<eb>lle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)

Initial release

2021-03-30