# Bayesian Outbreak Detection in the Presence of Reporting Delays

`bodaDelay.Rd`

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.

## 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) \((1-\alpha)\cdot 100\%\) prediction interval is calculated unlike the original method where it was a two-sided interval. The upper limit of this interval i.e. the \((1-\alpha)\cdot 100\%\) 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

```
if (FALSE) { # \dontrun{
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)
} # }
```