surveillance (version 1.23.1)

algo.farrington: Surveillance for Count Time Series Using the Classic Farrington Method

Description

Implements the procedure of Farrington et al. (1996). At each time point of the specified range, a GLM is fitted to predict the counts. This is then compared to the observed counts. If the observation is above a specific quantile of the prediction interval, then an alarm is raised.

Usage

# original interface for a single "disProg" time series
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"))
# wrapper for "sts" data, possibly multivariate
farrington(sts, control=list(
    range=NULL, b=5, w=3, reweight=TRUE, verbose=FALSE,
    alpha=0.05), ...)

Value

For algo.farrington, a list object of class "survRes"

with elements alarm, upperbound, trend, disProgObj, and control.

For farrington, the input "sts" object with updated alarm, upperbound and control slots, and subsetted to control$range.

Arguments

disProgObj

an object of class "disProg" (a list including observed and 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) \((1-\alpha)\) 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.

sts

an object of class "sts".

...

arguments for wrap.algo, e.g., verbose=FALSE.

Author

M. Höhle

Details

The following steps are performed according to the Farrington et al. (1996) paper.

1. fit of the initial model and initial estimation of mean and overdispersion.

2. calculation of the weights omega (correction for past outbreaks)

3. refitting of the model

4. revised estimation of overdispersion

5. rescaled model

6. omission of the trend, if it is not significant

7. repetition of the whole procedure

8. calculation of the threshold value

9. computation of exceedance score

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.

See Also

algo.farrington.fitGLM, algo.farrington.threshold

An improved Farrington algorithm is available as function farringtonFlexible.

Examples

#load "disProg" data
data("salmonella.agona")

#Do surveillance for the last 42 weeks
n <- length(salmonella.agona$observed)
control <- list(b=4,w=3,range=(n-42):n,reweight=TRUE, verbose=FALSE,alpha=0.01)
res <- algo.farrington(salmonella.agona,control=control)
plot(res)

#Generate Poisson counts and create an "sts" object
set.seed(123)
x <- rpois(520,lambda=1)
stsObj <- sts(observed=x, frequency=52)

if (surveillance.options("allExamples")) {
#Compare timing of the two possible fitters for algo.farrington
  range <- 312:520
  system.time( sts1 <- farrington(stsObj, control=list(range=range,
                         fitFun="algo.farrington.fitGLM.fast"), verbose=FALSE))
  system.time( sts2 <- farrington(stsObj, control=list(range=range,
                         fitFun="algo.farrington.fitGLM"), verbose=FALSE))
  #Check if results are the same
  stopifnot(upperbound(sts1) == upperbound(sts2))
}