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Weekly number of influenza A & B cases in the 140 districts of the two Southern German states Bavaria and Baden-Wuerttemberg, for the years 2001 to 2008. These surveillance data have been analyzed originally by Paul and Held (2011) and more recently by Meyer and Held (2014).

## Usage

data(fluBYBW)

## Format

An sts object containing $$416\times 140$$ observations starting from week 1 in 2001.

The population slot contains the population fractions of each district at 31.12.2001, obtained from the Federal Statistical Office of Germany.

The map slot contains an object of class "SpatialPolygonsDataFrame".

## Source

Robert Koch-Institut: SurvStat: https://survstat.rki.de/; Queried on 6 March 2009.

## Note

Prior to surveillance version 1.6-0, data(fluBYBW) contained a redundant last row (417) filled with zeroes only.

## References

Paul, M. and Held, L. (2011) Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts. Statistics in Medicine, 30, 1118-1136.

Meyer, S. and Held, L. (2014): Power-law models for infectious disease spread. The Annals of Applied Statistics, 8 (3), 1612-1639. doi: 10.1214/14-AOAS743

## Examples

data("fluBYBW")
# Count time series plot
plot(fluBYBW, type = observed ~ time)
# Map of disease incidence (per 100000 inhabitants) for the year 2001
plot(fluBYBW, type = observed ~ unit, tps = 1:52, total.args = list(),
population = fluBYBW@map$X31_12_01 / 100000) # the overall rate for 2001 shown in the bottom right corner is sum(observed(fluBYBW[1:52,])) / sum(fluBYBW@map$X31_12_01) * 100000

if (FALSE) {
# Generating an animation takes a while.
# Here we take the first 20 weeks of 2001 (runtime: ~3 minutes).
# The full animation is available in Supplement A of Meyer and Held (2014)
if (require("animation")) {
oldwd <- setwd(tempdir())  # to not clutter up the current working dir
saveHTML(animate(fluBYBW, tps = 1:20),
title="Evolution of influenza in Bayern and Baden-Wuerttemberg",
ani.width=500, ani.height=600)
setwd(oldwd)
}
}