Data structure for continuous spatio-temporal event data, e.g. individual case reports of an infectious disease. Apart from the actual events, the class simultaneously holds a spatio-temporal grid of endemic covariates (similar to disease mapping) and a representation of the observation region.

The "epidataCS" class is the basis for fitting spatio-temporal endemic-epidemic intensity models with the function twinstim (Meyer et al., 2012). The implementation is described in Meyer et al. (2017, Section 3), see vignette("twinstim").

as.epidataCS(events, stgrid, W, qmatrix = diag(nTypes),
             nCircle2Poly = 32L, T = NULL,
             clipper = c("polyclip", "rgeos"), verbose = interactive())

# S3 method for epidataCS
print(x, n = 6L, digits = getOption("digits"), ...)

# S3 method for epidataCS
nobs(object, ...)
# S3 method for epidataCS
head(x, n = 6L, ...)
# S3 method for epidataCS
tail(x, n = 6L, ...)
# S3 method for epidataCS
[(x, i, j, ..., drop = TRUE)
# S3 method for epidataCS
subset(x, subset, select, drop = TRUE, ...)

# S3 method for epidataCS
marks(x, coords = TRUE, ...)

# S3 method for epidataCS
summary(object, ...)
# S3 method for summary.epidataCS
print(x, ...)

# S3 method for epidataCS
as.stepfun(x, ...)

getSourceDists(object, dimension = c("space", "time"))



a "SpatialPointsDataFrame" of cases with the following obligatory columns (in the events@data data.frame):


time point of event. Will be converted to a numeric variable by as.numeric. There should be no concurrent events (but see untie for an ex post adjustment) and there cannot be events beyond stgrid (i.e., time<=T is required). Events at or before time \(t_0\) = min(stgrid$start) are allowed and form the prehistory of the process.


the spatial region (tile) where the event is located. This links to the tiles of stgrid.


optional type of event in a marked twinstim model. Will be converted to a factor variable dropping unused levels. If missing, all events will be attribute the single type "1".


maximum temporal influence radius (e.g. length of infectious period, time to culling, etc.); must be positive and may be Inf.


maximum spatial influence radius (e.g. 100 [km]); must be positive and may be Inf. A compact influence region mainly has computational advantages, but might also be plausible for specific applications.

The data.frame may contain columns with further marks of the events, e.g. sex, age of infected individuals, which may be used as epidemic covariates influencing infectiousness. Note that some auxiliary columns will be added at conversion whose names are reserved: ".obsInfLength", ".bdist", ".influenceRegion", and ".sources", as well as "start", "BLOCK", and all endemic covariates' names from stgrid.


a data.frame describing endemic covariates on a full spatio-temporal region x interval grid (e.g., district x week), which is a decomposition of the observation region W and period \(t_0,T\). This means that for every combination of spatial region and time interval there must be exactly one row in this data.frame, that the union of the spatial tiles equals W, the union of the time intervals equals \(t_0,T\), and that regions (and intervals) are non-overlapping. There are the following obligatory columns:


ID of the spatial region (e.g., district ID). It will be converted to a factor variable (dropping unused levels if it already was one).

start, stop

columns describing the consecutive temporal intervals (converted to numeric variables by as.numeric). The start time of an interval must be equal to the stop time of the previous interval. The stop column may be missing, in which case it will be auto-generated from the set of start values and T.


area of the spatial region (tile). Be aware that the unit of this area (e.g., square km) must be consistent with the units of W and events (as specified in their proj4strings).

The remaining columns are endemic covariates. Note that the column name "BLOCK" is reserved (a column which will be added automatically for indexing the time intervals of stgrid).


an object of class "SpatialPolygons" representing the observation region. It must have the same proj4string as events and all events must be within W. Prior simplification of W may considerably reduce the computational burden of likelihood evaluations in twinstim models with non-trivial spatial interaction functions (see the “Note” section below).


a square indicator matrix (0/1 or FALSE/TRUE) for possible transmission between the event types. The matrix will be internally converted to logical. Defaults to an independent spread of the event types, i.e. the identity matrix.


accuracy (number of edges) of the polygonal approximation of a circle, see discpoly.


end of observation period (i.e. last stop time of stgrid). Must be specified if the start but not the stop times are supplied in stgrid (=> auto-generation of stop times).


polygon clipping engine to use for calculating the .influenceRegions of events (see the Value section below). Default is the polyclip package (called via intersect.owin from package spatstat.geom). In surveillance <= 1.6-0, package gpclib was used, which has a restrictive license. This is no longer supported.


logical indicating if status messages should be printed during input checking and "epidataCS" generation. The default is to do so in interactive R sessions.


an object of class "epidataCS" or "summary.epidataCS", respectively.


a single integer. If positive, the first (head, print) / last (tail) n events are extracted. If negative, all but the n first/last events are extracted.


minimum number of significant digits to be printed in values.


arguments passed to the [-method for SpatialPointDataFrames for subsetting the events while retaining stgrid and W.
If drop=TRUE (the default), event types that completely disappear due to i-subsetting will be dropped, which reduces qmatrix and the factor levels of the type column.
By the j index, epidemic covariates can be removed from events.


unused (arguments of the generics) with a few exceptions: The print method for "epidataCS" passes ... to the method, and the print method for "summary.epidataCS" passes additional arguments to print.table.

subset, select

arguments used to subset the events from an "epidataCS" object like in


logical indicating if the data frame of event marks returned by marks(x) should have the event coordinates appended as last columns. This defaults to TRUE.


an object of class "epidataCS".


the distances of all events to their potential source events can be computed in either the "space" or "time" dimension.


The function as.epidataCS is used to generate objects of class "epidataCS", which is the data structure required for twinstim models.

The [-method for class "epidataCS" ensures that the subsetted object will be valid, for instance, it updates the auxiliary list of potential transmission paths stored in the object. The [-method is used in subset.epidataCS, which is implemented similar to

The print method for "epidataCS" prints some metadata of the epidemic, e.g., the observation period, the dimensions of the spatio-temporal grid, the types of events, and the total number of events. By default, it also prints the first n = 6 rows of the events.


An object of class "epidataCS" is a list containing the following components:


a "SpatialPointsDataFrame" (see the description of the argument). The input events are checked for requirements and sorted chronologically. The columns are in the following order: obligatory event columns, event marks, the columns BLOCK, start and endemic covariates copied from stgrid, and finally, hidden auxiliary columns. The added auxiliary columns are:


observed length of the infectious period (possibly truncated at T), i.e., pmin(T-time, eps.t).


a list of numeric vectors of potential sources of infection (wrt the interaction ranges eps.s and eps.t) for each event. Row numbers are used as index.


minimal distance of the event locations to the polygonal boundary W.


a list of influence regions represented by objects of the spatstat.geom class "owin". For each event, this is the intersection of W with a (polygonal) circle of radius eps.s centered at the event's location, shifted such that the event location becomes the origin. The list has nCircle2Poly set as an attribute.


a data.frame (see description of the argument). The spatio-temporal grid of endemic covariates is sorted by time interval (indexed by the added variable BLOCK) and region (tile). It is a full BLOCK x tile grid.


a "SpatialPolygons" object representing the observation region.


see the above description of the argument. The storage.mode of the indicator matrix is set to logical and the dimnames are set to the levels of the event types.

The nobs-method returns the number of events. The head and tail methods subset the epidemic data using the extraction method ([), i.e. they return an object of class "epidataCS", which only contains (all but) the first/last n events. For the "epidataCS" class, the method of the generic function marks defined by the spatstat.geom package returns a data.frame of the event marks (actually also including time and location of the events), disregarding endemic covariates and the auxiliary columns from the events component of the "epidataCS" object. The summary method (which has again a print method) returns a list of metadata, event data, the tables of tiles and types, a step function of the number of infectious individuals over time ($counter), i.e., the result of the as.stepfun-method for "epidataCS", and the number of potential sources of transmission for each event ($nSources) which is based on the given maximum interaction ranges eps.t and eps.s.


Since the observation region W defines the integration domain in the point process likelihood, the more detailed the polygons of W are the longer it will take to fit a twinstim. You are advised to sacrifice some shape details for speed by reducing the polygon complexity, for example via the mapshaper JavaScript library wrapped by the R package rmapshaper. Alternative tools are provided by the packages maptools (thinnedSpatialPoly) and spatstat.geom (simplify.owin).


Meyer, S., Elias, J. and Höhle, M. (2012): A space-time conditional intensity model for invasive meningococcal disease occurrence. Biometrics, 68, 607-616. doi: 10.1111/j.1541-0420.2011.01684.x

Meyer, S., Held, L. and Höhle, M. (2017): Spatio-temporal analysis of epidemic phenomena using the R package surveillance. Journal of Statistical Software, 77 (11), 1-55. doi: 10.18637/jss.v077.i11


Sebastian Meyer

Contributions to this documentation by Michael Höhle and Mayeul Kauffmann.

See also


plot.epidataCS for plotting, and animate.epidataCS for the animation of such an epidemic. There is also an update method for the "epidataCS" class.

To re-extract the events point pattern from "epidataCS", use as(object, "SpatialPointsDataFrame").

It is possible to convert an "epidataCS" point pattern to an "epidata" object (as.epidata.epidataCS), or to aggregate the events into an "sts" object (epidataCS2sts).


## load "imdepi" example data (which is an object of class "epidataCS")

## print and summary
print(imdepi, n=5, digits=2)
print(s <- summary(imdepi))
plot(s$counter,  # same as 'as.stepfun(imdepi)'
     xlab = "Time [days]", ylab="Number of infectious individuals",
     main=paste("Time course of the number of infectious individuals",
                "assuming an infectious period of 30 days", sep="\n"))
plot(table(s$nSources), xlab="Number of \"close\" infective individuals",
     ylab="Number of events",
     main=paste("Distribution of the number of potential sources",
                "assuming an interaction range of 200 km and 30 days",
## the summary object contains further information

## a histogram of the spatial distances to potential source events
## (i.e., to events of the previous eps.t=30 days within eps.s=200 km)
sourceDists_space <- getSourceDists(imdepi, "space")
hist(sourceDists_space); rug(sourceDists_space)

## internal structure of an "epidataCS"-object
str(imdepi, max.level=4)
## see help("imdepi") for more info on the data set

## extraction methods subset the 'events' component
## (thereby taking care of the validity of the epidataCS object,
## for instance the hidden auxiliary column .sources)
tail(imdepi, n=4)           # reduce the epidemic to the last 4 events
subset(imdepi, type=="B")   # only consider event type B

## see help("plot.epidataCS") for convenient plot-methods for "epidataCS"

### reconstruct the "imdepi" object

## observation region
load(system.file("shapes", "districtsD.RData", package="surveillance"),
     verbose = TRUE)

## extract point pattern of events from the "imdepi" data
events <- marks(imdepi)  # data frame with coordinate columns
coordinates(events) <- c("x", "y")  # promote to a "SpatialPointsDataFrame"
#proj4string(events) <- proj4string(stateD)
events@proj4string <- stateD@proj4string  # exact copy (avoid CRS reformatting)

## or, much simpler, use the corresponding coerce-method
# \dontshow{
events@coords.nrs <- numeric(0L)
stopifnot(all.equal(as(imdepi, "SpatialPointsDataFrame"), events))
# }
events <- as(imdepi, "SpatialPointsDataFrame")

## plot observation region with events
plot(stateD, axes=TRUE); title(xlab="x [km]", ylab="y [km]")
points(events, pch=unclass(events$type), cex=0.5, col=unclass(events$type))
legend("topright", legend=levels(events$type), title="Type", pch=1:2, col=1:2)

## space-time grid with endemic covariates
head(stgrid <- imdepi$stgrid[,-1])

## reconstruct the "imdepi" object from its components
myimdepi <- as.epidataCS(events = events, stgrid = stgrid,
                         W = stateD, qmatrix = diag(2), nCircle2Poly = 16)
if (FALSE) {
## This reconstructed object is equal to 'imdepi' as long as the internal
## structures of the embedded classes ("owin", "SpatialPolygons", ...), and
## the calculation of the influence regions by "polyclip" have not changed:
stopifnot(all.equal(imdepi, myimdepi))