  The availability of geocoded health data and the inherent temporal structure
of communicable diseases have led to an increased interest in statistical
models and software for spatio-temporal data with epidemic features. The open
source R package surveillance can handle various levels of aggregation at which
infective events have been recorded: individual-level time-stamped
geo-referenced data (case reports) in either continuous space or discrete
space, as well as counts aggregated by period and region. For each of these
data types, the surveillance package implements tools for visualization,
likelihoood inference and simulation from recently developed statistical
regression frameworks capturing endemic and epidemic dynamics. Altogether, this
paper is a guide to the spatio-temporal modeling of epidemic phenomena,
exemplified by analyses of public health surveillance data on measles and
invasive meningococcal disease.
