as.epidata is used to generate objects
of class "epidata". Objects of this class are
specific data frames containing the event history of an epidemic together
with some additional attributes. These objects are the basis for fitting
spatio-temporal epidemic intensity models with the function
twinSIR. Note that the spatial information itself, i.e.
the positions of the individuals, is assumed to be constant over time.
Besides epidemics following the SIR compartmental model, also data from SI,
SIRS and SIS epidemics may be supplied. Inference for the infectious process
works as usual and simulation of such epidemics is also possible.as.epidata(data, ...)## S3 method for class 'data.frame':
as.epidata(data, t0, tE.col, tI.col, tR.col,
id.col, coords.cols, f = list(), w = list(), keep.cols = TRUE, ...)
## S3 method for class 'default':
as.epidata(data, id.col, start.col, stop.col, atRiskY.col,
event.col, Revent.col, coords.cols, f = list(), w = list(), ...)
## S3 method for class 'epidata':
print(x, ...)
## S3 method for class 'epidata':
[(x, i, j, drop)
## S3 method for class 'epidata':
update(object, f = list(), w = list(), ...)
data.frame-method, a data frame with as many rows as
there are individuals in the population and time columns indicating
when each individual became exposed (optional), infectious
(mandatory, but can be NA fortE.col, tI.col, tR.col.
Individuals that have already been removed prior to t0, i.e.,
rows with tR data, which specify when the individuals became exposed,
infectious and removed, respectively.
tE.col and tR.col can be missing, corresponding toid column in data.
The id column identifies the individuals in the data frame.
It is converted to a factor by calling factorstart column in data. Can be numeric
(by column number) or character (by column name).
The start column contains the (numeric) time points of the beginnings
of the consecutive time instop column in data. Can be numeric
(by column number) or character (by column name).
The stop column contains the (numeric) time points of the ends
of the consecutive time intervals atRiskY column in data. Can be numeric
(by column number) or character (by column name).
The atRiskY column indicates if the individual was event column in data. Can be numeric
(by column number) or character (by column name).
The event column indicates if the individual became infected
at the stop tRevent column in data. Can be numeric
(by column number) or character (by column name).
The Revent column indicates if the individual was recovered
at the stop coords columns in data. Can be
a numeric (by column number) vector, a character (by column name) vector
or NULL (in which case epidemic covariates are not calculateable).
data, which is determined by the name of tdata
should be retained (and not only the obligatory "epidata"
columns), in particular any additional columns with
time-constant individual-specific covariates.
Alternatively,"epidata".print.data.frame. Currently unused
in the as.epidata-methods.[.data.frame.data.frame with the columns "BLOCK", "id",
"start", "stop", "atRiskY", "event",
"Revent" and the coordinate columns (with the original names from
data), which are all obligatory. These columns are followed by any
remaining columns of the input data. Last but not least, the newly
generated columns with epidemic variables corresponding to the functions
in the list f are appended, if length(f) > 0.
The data.frame is given the additional attributesc(min(start), max(stop)).f.w.print method for objects of class "epidata" simply prints
the data frame with a small header containing the time range of the observed
epidemic and the number of infected individuals. Usually, the data frames
are quite long, so the summary method summary.epidata might be
useful. Also, indexing/subsetting "epidata" works exactly as for
data.frames, but there is an own method, which
assures consistency of the resulting "epidata" or drops this class, if
necessary.
The update-method can be used to add or replace distance-based
(f) or covariate-based (w) epidemic variables in an
existing "epidata" object.
SIS epidemics are implemented as SIRS epidemics where the length of the
removal period equals 0. This means that an individual, which has an R-event
will be at risk immediately afterwards, i.e. in the following time block.
Therefore, data of SIS epidemics have to be provided in that form containing
hagelloch data for a "epidata" object.
The code for the conversion from the simple data frame to the SIR event
history using as.epidata.data.frame is given in
example(hagelloch).The plot and the
summary method for class "epidata".
Furthermore, the function animate.epidata for the animation of
epidemics.
Function twinSIR for fitting spatio-temporal epidemic intensity
models to epidemic data.
Function simEpidata for the simulation of epidemic data.
# see example(hagelloch)
# here is an artificial event history
data("foodata")
str(foodata)
# convert the data to an object of class "epidata",
# also generating some epidemic covariates
myEpidata <- as.epidata(foodata,
id.col = 1, start.col = "start", stop.col = "stop",
atRiskY.col = "atrisk", event.col = "infected", Revent.col = "removed",
coords.cols = c("x","y"),
f = list(B1 = function(u) u <= 1, B2 = function(u) u > 1))
# this is how data("fooepidata") has been generated
data("fooepidata")
stopifnot(all.equal(myEpidata, fooepidata))
# add covariate-based weight for the force of infection, e.g.,
# to model an increased force if i and j have the same value in z1
myEpidata2 <- update(fooepidata,
w = list(samez1 = function(z1.i, z1.j) z1.i == z1.j))
str(fooepidata)
subset(fooepidata, BLOCK == 1)
summary(fooepidata) # see 'summary.epidata'
plot(fooepidata) # see 'plot.epidata' and also 'animate.epidata'
stateplot(fooepidata, "15") # see 'stateplot'Run the code above in your browser using DataLab