surveillance: epidataCS_aggregate – R documentation

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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 class 'epidataCS'
as.epidata(data, tileCentroids, eps = 0.001, ...)

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).

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.

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).

Author(s)

Sebastian Meyer

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)

if (surveillance.options("allExamples")) {
    ## plot number of cases by district
    plot(imdsts, 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)
}

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