surveillance-package: [stage=build]{(meta <- packageDescription("surveillance", encoding="latin1"))$Title}The surveillance package implements statistical methods for the
retrospective modeling and prospective monitoring of epidemic phenomena
in temporal and spatio-temporal contexts.
Focus is on (routinely collected) public health surveillance data,
but the methods just as well apply to data from environmetrics,
econometrics or the social sciences. As many of the monitoring methods
rely on statistical process control methodology, the package is
also relevant to quality control and reliability engineering.
ll{
Package: [stage=build]{meta$Package}
Version: [stage=build]{meta$Version}
License: [stage=build]{meta$License}
URL: http://surveillance.r-forge.r-project.org/
The package implements many typical outbreak detection procedures such
as Stroup et al. (1989), Farrington et al., (1996), Rossi et al. (1999),
Rogerson and Yamada (2001), a Bayesian approach (H�{oe}hle, 2007),
negative binomial CUSUM methods (H�{oe}hle and Mazick, 2009), and a
detector based on generalized likelihood ratios (H�{oe}hle
and Paul, 2008). However, also CUSUMs for the prospective change-point
detection in binomial, beta-binomial and multinomial time series is
covered based on generalized linear modeling. This includes,
e.g., paired binary CUSUM described by Steiner et al. (1999) or paired
comparison Bradley-Terry modeling described in H�{oe}hle
(2010). The package contains several real-world datasets, the ability
to simulate outbreak data, visualize the results of the monitoring in
temporal, spatial or spatio-temporal fashion. In dealing with time
series data, the fundamental data structure of the package is the S4
class sts wrapping observations, monitoring results and
date handling for multivariate time series.
A recent overview of the available monitoring procedures is
given by Salmon et al. (2016).
For the retrospective analysis of epidemic spread, the package
provides three endemic-epidemic modeling frameworks with
tools for visualization, likelihood inference, and simulation.
The function hhh4 offers inference methods for the
(multivariate) count time series models of Held et al. (2005), Paul et
al. (2008), Paul and Held (2011), Held and Paul (2012), and Meyer and
Held (2014). See vignette("hhh4") for a general introduction
and vignette("hhh4_spacetime") for a discussion and
illustration of spatial hhh4 models.
Furthermore, the fully Bayesian approach for univariate
time series of counts from Held et al. (2006) is implemented as
function algo.twins.
Self-exciting point processes are modeled through endemic-epidemic
conditional intensity functions.
twinSIR (H�{oe}hle, 2009) models the
susceptible-infectious-recovered (SIR) event history of a
fixed population, e.g, epidemics across farms or networks;
see vignette("twinSIR") for an illustration.
twinstim (Meyer et al., 2012) fits spatio-temporal point
process models to point patterns of infective events, e.g.,
time-stamped geo-referenced surveillance data on infectious disease
occurrence; see vignette("twinstim") for an illustration.
A recent overview of the implemented space-time modeling frameworks
for epidemic phenomena is given by Meyer et al. (2016).[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]