surveillance (version 1.24.0)

epidataCS_aggregate: Conversion (aggregation) of "epidataCS" to "epidata" or "sts"

Description

Continuous-time continuous-space epidemic data stored in an object of class "epidataCS" can be aggregated in space or in space and time yielding an object of class "epidata" or "sts" for use of twinSIR or hhh4 modelling, respectively.

Usage

## aggregation in space and time over 'stgrid' for use of 'hhh4' models
epidataCS2sts(object, freq, start, neighbourhood,
              tiles = NULL, popcol.stgrid = NULL, popdensity = TRUE)
## aggregation in space for use of 'twinSIR' models
# S3 method for epidataCS
as.epidata(data, tileCentroids, eps = 0.001, ...)

Value

epidataCS2sts: an object of class "sts"

representing the multivariate time-series of the number of cases aggregated over stgrid.

as.epidata.epidataCS: an object of class

"epidata" representing an SIS epidemic in form of a multivariate point process (one for each region/tile).

Arguments

object, data

an object of class "epidataCS".

freq,start

see the description of the "sts" class. The start specification should reflect the beginning of object$stgrid, i.e., the start of the first time interval.

neighbourhood

binary adjacency or neighbourhood-order matrix of the regions (tiles). If missing but tiles is given, a binary adjacency matrix will be auto-generated from tiles using functionality of the spdep package (see poly2adjmat). Since the "neighbourhood" slot in "sts" is actually optional, neighbourhood=NULL also works.

tiles

object inheriting from "SpatialPolygons" representing the regions in object$stgrid (column "tile"). It will become the "map" slot of the resulting "sts" object. Its row.names must match levels(object$stgrid$tile). If neighbourhood is provided, tiles is optional (not required for hhh4, but for plots of the resulting "sts" object).

popcol.stgrid

single character or numeric value indexing the column in object$stgrid which contains the population data (counts or densities, depending on the popdensity argument). This will become the "populationFrac" slot (optional).

popdensity

logical indicating if the column referenced by popcol.stgrid contains population densities or absolute counts.

tileCentroids

a coordinate matrix of the region centroids (i.e., the result of coordinates(tiles)). Its row names must match levels(data$stgrid$tile). This will be the coordinates used for the “population” (i.e., the tiles from "epidataCS") in the discrete-space twinSIR modelling.

eps

numeric scalar for breaking tied removal and infection times between different individuals (tiles), which might occur during conversion from "epidataCS" to "epidata". Rather dumb, this is simply done by subtracting eps from each tied removal time. One should consider other ways of breaking the tied event times.

...

unused (argument of the generic).

Author

Sebastian Meyer

Details

Conversion to "sts" only makes sense if the time intervals (BLOCKs) of the stgrid are regularly spaced (to give freq intervals per year). Note that events of the prehistory (not covered by stgrid) are not included in the resulting sts object.

Some comments on the conversion to "epidata": the conversion results into SIS epidemics only, i.e. the at-risk indicator is set to 1 immediately after recovery. A tile is considered infective if at least one individual within the tile is infective, otherwise it is susceptible. The lengths of the infectious periods are taken from data$events$eps.t. There will be no f columns in the resulting "epidata". These must be generated by a subsequent call to as.epidata with desired f.

See Also

epidata and twinSIR

linkS4class{sts} and hhh4.

Examples

data("imdepi")
load(system.file("shapes", "districtsD.RData", package="surveillance"))

## convert imdepi point pattern into multivariate time series
imdsts <- epidataCS2sts(imdepi, freq = 12, start = c(2002, 1),
                        neighbourhood = NULL, # not needed here
                        tiles = districtsD)

## check the overall number of events by district
stopifnot(all.equal(colSums(observed(imdsts)),
                    c(table(imdepi$events$tile))))

## compare plots of monthly number of cases
opar <- par(mfrow = c(2, 1))
plot(imdepi, "time")
plot(imdsts, type = observed ~ time)
par(opar)

## plot number of cases by district in Bavaria (municipality keys 09xxx)
imd09 <- imdsts[, grep("^09", colnames(imdsts), value = TRUE), drop = TRUE]
plot(imd09, type = observed ~ unit)

## also test conversion to an SIS event history ("epidata") of the "tiles"
if (requireNamespace("intervals")) {
    imdepi_short <- subset(imdepi, time < 50)  # to reduce the runtime
    imdepi_short$stgrid <- subset(imdepi_short$stgrid, start < 50)
    imdepidata <- as.epidata(imdepi_short,
                             tileCentroids = coordinates(districtsD))
    summary(imdepidata)
}