surveillance: bodaDelay – R documentation

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bodaDelay

Bayesian Outbreak Detection in the Presence of Reporting Delays

Description

The function takes range values of the surveillance time series sts and for each time point uses a Bayesian model of the negative binomial family with log link inspired by the work of Noufaily et al. (2012) and of Manitz and Höhle (2014). It allows delay-corrected aberration detection as explained in Salmon et al. (2015). A reportingTriangle has to be provided in the control slot.

Usage

bodaDelay(sts, control = list(
  range = NULL, b = 5, w = 3, mc.munu = 100, mc.y = 10,
  pastAberrations = TRUE, verbose = FALSE,
  alpha = 0.05, trend = TRUE, limit54 = c(5,4), 
  inferenceMethod = c("asym","INLA"), quantileMethod = c("MC","MM"),
  noPeriods = 1, pastWeeksNotIncluded = NULL, delay = FALSE))

Arguments

sts

sts-object to be analysed. Needs to have a reporting triangle.

control

list of control arguments:

b: How many years back in time to include when forming the base counts.
w: Window's half-size, i.e. number of weeks to include before and after the current week in each year.
range: Specifies the index of all timepoints which should be tested. If range is NULL all possible timepoints are used.
pastAberrations: Boolean indicating whether to include an effect for past outbreaks in a second fit of the model. This option only makes sense if inferenceMethod is INLA, as it is not supported by the other inference method.
verbose: Boolean specifying whether to show extra debugging information.
alpha: An approximate (one-sided) (1-α)\cdot 100\% prediction interval is calculated unlike the original method where it was a two-sided interval. The upper limit of this interval i.e. the (1-α)\cdot 100\% quantile serves as an upperbound.
trend: Boolean indicating whether a trend should be included
noPeriods: Number of levels in the factor allowing to use more baseline. If equal to 1 no factor variable is created, the set of reference values is defined as in Farrington et al (1996).
inferenceMethod: Which inference method used, as defined in Salmon et al. (2015). If one chooses "INLA" then inference is performed with INLA. If one chooses "asym" (default) then the asymptotic normal approximation of the posteriori is used.
pastWeeksNotIncluded: Number of past weeks to ignore in the calculation. The default (NULL) means to use the value of control$w.
delay: Boolean indicating whether to take reporting delays into account.
mc.munu: Number of samples for the parameters of the negative binomial distribution for calculating a threshold
mc.y: Number of samples for observations when performing Monte Carlo to calculate a threshold
limit54: c(cases,period) is a vector allowing the user to change these numbers.
quantileMethod: Character, either "MC" (default) or "MM". Indicates how to compute the quantile based on the posterior distribution (no matter the inference method): either by sampling mc.munu values from the posterior distribution of the parameters and then for each sampled parameters vector sampling mc.y response values so that one gets a vector of response values based on which one computes an empirical quantile (MC method, as explained in Salmon et al. 2015); or by sampling mc.munu from the posterior distribution of the parameters and then compute the quantile of the mixture distribution using bisectioning, which is faster.

References

Farrington, C.P., Andrews, N.J, Beale A.D. and Catchpole, M.A. (1996): A statistical algorithm for the early detection of outbreaks of infectious disease. J. R. Statist. Soc. A, 159, 547-563.

Noufaily, A., Enki, D.G., Farrington, C.P., Garthwaite, P., Andrews, N.J., Charlett, A. (2012): An improved algorithm for outbreak detection in multiple surveillance systems. Statistics in Medicine, 32 (7), 1206-1222.

Salmon, M., Schumacher, D., Stark, K., Höhle, M. (2015): Bayesian outbreak detection in the presence of reporting delays. Biometrical Journal, 57 (6), 1051-1067.

Examples

## Not run: 
data("stsNewport")
salm.Normal <- list()
salmDelayAsym <- list()
for (week in 43:45){
  listWeeks <- as.Date(row.names(stsNewport@control$reportingTriangle$n))
  dateObs <- listWeeks[isoWeekYear(listWeeks)$ISOYear==2011 &
                       isoWeekYear(listWeeks)$ISOWeek==week]
  stsC <- sts_observation(stsNewport,
                          dateObservation=dateObs,
                          cut=TRUE)
  inWeeks <- with(isoWeekYear(epoch(stsC)),
                  ISOYear == 2011 & ISOWeek >= 40 & ISOWeek <= 48)
  
  rangeTest <- which(inWeeks)
  alpha <- 0.07

  # Control slot for Noufaily method          
  controlNoufaily <- list(range=rangeTest,noPeriods=10,
                          b=4,w=3,weightsThreshold=2.58,pastWeeksNotIncluded=26,
                          pThresholdTrend=1,thresholdMethod="nbPlugin",alpha=alpha*2,
                          limit54=c(0,50))
  
  # Control slot for the Proposed algorithm with D=0 correction
  controlNormal <- list(range = rangeTest, b = 4, w = 3,
                        reweight = TRUE, mc.munu=10000, mc.y=100,
                        verbose = FALSE,
                        alpha = alpha, trend = TRUE,
                        limit54=c(0,50), 
                        noPeriods = 10, pastWeeksNotIncluded = 26,
                        delay=FALSE)
  
  # Control slot for the Proposed algorithm with D=10 correction
  controlDelayNorm <-  list(range = rangeTest, b = 4, w = 3,
                            reweight = FALSE, mc.munu=10000, mc.y=100,
                            verbose = FALSE,
                            alpha = alpha, trend = TRUE,
                            limit54=c(0,50), 
                            noPeriods = 10, pastWeeksNotIncluded = 26,
                            delay=TRUE,inferenceMethod="asym")
  
  set.seed(1)
  salm.Normal[[week]] <- farringtonFlexible(stsC, controlNoufaily)
  salmDelayAsym[[week]] <- bodaDelay(stsC, controlDelayNorm)
}

opar <- par(mfrow=c(2,3))
lapply(salmDelayAsym[c(43,44,45)],plot, legend=NULL, main="", ylim=c(0,35))
lapply(salm.Normal[c(43,44,45)],plot, legend=NULL, main="", ylim=c(0,35))
par(opar)

## 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