surveillance (version 1.7-0)

imdepi: Occurrence of Invasive Meningococcal Disease in Germany

Description

imdepi contains data on the spatio-temporal location of 636 cases of invasive meningococcal disease caused by two specific meningococcal finetypes in Germany. imdepifit contains a model fit to the imdepi data.

Usage

data(imdepi)
data(imdepifit)

encoding

latin1

format

imdepi is an object of class "epidataCS" (a list with components events, stgrid, W and qmatrix). imdepifit is an object of class "twinstim", see summary.twinstim for some simple methods for fitted "twinstim" models.

source

German Reference Centre for Meningococci (NRZM) -- hosted by the Department of Hygiene and Microbiology, Julius-Maximilians-Universit�{ae}t W�{ue}rzburg, Germany. Thanks to Dr. Johannes Elias and Prof. Dr. Ulrich Vogel from the NRZM for providing the IMD event data. See http://www.meningococcus.de/ and http://episcangis.hygiene.uni-wuerzburg.de/ for further details about the IMD event data.

Details

The dataset contains a slightly modified version of the data analyzed in Meyer et al. (2012) represented as an "epidataCS" object, which is a list with components events, stgrid, W, and qmatrix.

The events entry is a "SpatialPointsDataFrame" object (ETSR89 projection, i.e. EPSG code 3035, with unit km) containing 636 events, each with the following entries: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object] There are further auxiliary columns attached to the events' data the names of which begin with a . (dot): These are created during conversion to the "epidataCS" class and are necessary for fitting the data with twinstim, see the description of the "epidataCS"-class. With coordinates(imdepi$events) one obtains the (x,y) locations of the events.

The identifier in tile is indexed according to the German official municipality key ( Amtlicher Gemeindeschl�{ue}ssel). See http://de.wikipedia.org/wiki/Amtlicher_Gemeindeschl%C3%BCssel for details. The data component stgrid contains the spatio-temporal grid of endemic covariate information. In addition to the usual bookkeeping variables this includes: [object Object],[object Object] We have actually not included any time-dependent covariates here, we just established this grid with a (reduced -> fast) temporal resolution of monthly intervals so that we can model endemic time trends and seasonality (in this discretized time). The entry W contains the observation window as a SpatialPolygons object. In this case the boundaries of Germany (simplified polygon to enhance speed). The entry qmatrix is a $2\times 2$ identity matrix indicating that no transmission between the two finetypes can occur.

References

Meyer, S., Elias, J. and H�{oe}hle, M. (2012): A space-time conditional intensity model for invasive meningococcal disease occurrence. Biometrics, 68, 607-616. DOI-Link: http://dx.doi.org/10.1111/j.1541-0420.2011.01684.x

Meyer, S. (2010): Spatio-Temporal Infectious Disease Epidemiology based on Point Processes. Master's Thesis, Ludwig-Maximilians-Universit�{ae}t M�{ue}nchen. Available as http://epub.ub.uni-muenchen.de/11703/

See Also

the data class "epidataCS", and function twinstim for model fitting.

Examples

data("imdepi")

# Basic information
print(imdepi, n=5, digits=2)

# What is an epidataCS-object?
str(imdepi, max.level=4)
names(imdepi$events@data)
# => events data.frame has hidden columns
sapply(imdepi$events@data, class)
# marks and print methods ignore these auxiliary columns

# look at the B type only
imdepiB <- subset(imdepi, type == "B")
#<- subsetting applies to the 'events' component
imdepiB

# select only the last 10 events
tail(imdepi, n=10)   # there is also a corresponding 'head' method

# Access event marks
str(marks(imdepi))

# there is an update-method which assures that the object remains valid
# when changing parameters like eps.s, eps.t or qmatrix
update(imdepi, eps.t = 20)

# Summary
s <- summary(imdepi)
s
str(s)

# Step function of number of infectives
plot(s$counter, xlab = "Time [days]",
     ylab = "Number of infectious individuals",
     main = "Time series of IMD assuming 30 days infectious period")

# distribution of number of potential sources of infection
opar <- par(mfrow=c(1,2), las=1)
for (type in c("B","C")) {
  plot(100*prop.table(table(s$nSources[s$eventTypes==type])),
  xlim=range(s$nSources), xlab = "Number of potential epidemic sources",
  ylab = "Proportion of events [%]")
}
par(opar)

# a histogram of the number of events along time (using the
# plot-method for the epidataCS-class, see ?plot.epidataCS)
opar <- par(mfrow = c(2,1))
plot(imdepi, aggregate = "time", subset = type == "B", main = "Finetype B")
plot(imdepi, aggregate = "time", subset = type == "C", main = "Finetype C")
par(opar)

# Plot the spatial distribution of the events in W
plot(imdepi$W)
colTypes <- c("darkblue","indianred2")
pch <- c(3,4)
with(imdepi, {
  plot(events,pch=pch[events$type],col=colTypes[events$type],add=TRUE)
  legend(x="topleft",levels(events$type),col=colTypes, pch=pch,title="Finetype")
})

# somehow easier: use the plot-method for the epidataCS class
plot(imdepi, aggregate="space", axes=TRUE, colTypes=colTypes)
title(xlab = "x [km]", ylab = "y [km]")

# Show a dynamic illustration of the spatio-temporal dynamics of the 
  # spread during the first year of type B with a step size of 7 days
  animate(imdepiB, interval=c(0,365), time.spacing=7, sleep=0.1)