| noroBE | R Documentation |
"sts" Objects from the Berlin Norovirus DataThe function noroBE() creates an "sts" object
based on the array of norovirus surveillance counts, the map
of Berlin's city district, and the pop2011 data stored in the
package. This is the data analysed by Meyer and Held (2017).
noroBE(
by = c("districts", "agegroups", "all", "none"),
agegroups = c(1, 2, 2, 4, 4, 2),
timeRange = c("2011-w27", "2015-w26"),
flatten = FALSE
)
counts
map
by |
character string determining the stratification, i.e., which units
the resulting
|
agegroups |
how the age groups in |
timeRange |
character vector of length two determining the time range
of the |
flatten |
logical indicating whether for |
an integer-valued array of norovirus surveillance counts
with labelled dimensions of size
290 ("week") x 12 ("district") x 15 ("agegroup").
a "SpatialPolygonsDataFrame"
of length 12 with row.names(map) matching colnames(counts),
representing Berlin's city districts in longlat coordinates (WGS84).
The data slot contains the full "NAME"s of the city districts
as well as their "POPULATION", i.e., rowSums(pop2011).
The function noroBE() returns an "sts" object
generated from these data (and pop2011).
Sebastian Meyer
based on norovirus surveillance counts retrieved from the SurvStat@RKI 2.0 online service (https://survstat.rki.de) of Germany's public health institute, the Robert Koch Institute, as of 2016-09-08.
based on a KML file of Berlin's 97 local centres
(“Ortsteile”) downloaded from the Berlin Open Data repository at
https://daten.berlin.de/datensaetze/geometrien-der-ortsteile-von-berlin-juli-2012
as of 2014-11-12, published by
Amt fuer Statistik Berlin-Brandenburg
(Statistical Office of Berlin-Brandenburg)
under the ‘CC BY 3.0 DE’ license
(https://creativecommons.org/licenses/by/3.0/de/).
The map included here aggregates
these local centres by city district.
Meyer S and Held L (2017): Incorporating social contact data in spatio-temporal models for infectious disease spread. Biostatistics, 18 (2), 338-351. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biostatistics/kxw051")}
## the raw data
str(counts)
summary(map)
## district-specific time series
noroBEr <- noroBE(by = "districts")
plot(noroBEr)
## age group-specific time series
noroBEg <- noroBE(by = "agegroups")
plot(noroBEg)
## list of spatio-temporal surveillance counts, one for each age group
noroBErbyg <- noroBE(by = "all", flatten = FALSE)
plot(noroBErbyg[[1L]], par.list = list(oma=c(0,0,2,0)))
title(main = names(noroBErbyg)[1], outer = TRUE, line = -1)
## flattened "sts" object (the 'neighbourhood' only reflects spatial info)
noroBEall <- noroBE(by = "all", flatten = TRUE)
dev.new(width = 16, height = 7)
plot(noroBEall, par.list = list(
xaxt = "n", mar = c(1,4,1,1), mfrow = c(ncol(noroBEg), ncol(noroBEr))
))
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