surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena
The R package surveillance 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.
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 (Höhle, 2007),
negative binomial CUSUM methods (Höhle and Mazick, 2009), and a
detector based on generalized likelihood ratios (Höhle
and Paul, 2008), see
Also CUSUMs for the prospective change-point detection in binomial,
beta-binomial and multinomial time series are covered based on
generalized linear modeling, see
This includes, e.g., paired comparison Bradley-Terry modeling described
in Höhle (2010), or paired binary CUSUM
pairedbinCUSUM) described by Steiner et al. (1999).
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
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.
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
vignette("hhh4_spacetime") for a discussion and
illustration of spatial
Furthermore, the fully Bayesian approach for univariate
time series of counts from Held et al. (2006) is implemented as
Self-exciting point processes are modeled through endemic-epidemic
conditional intensity functions.
twinSIR (Höhle, 2009) models the
susceptible-infectious-recovered (SIR) event history of a
fixed population, e.g, epidemics across farms or networks;
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
vignette("twinstim") for an illustration.
A recent overview of the implemented space-time modeling frameworks
for epidemic phenomena is given by Meyer et al. (2017).
Michael Hoehle, Sebastian Meyer, Michaela Paul
Maintainer: Sebastian Meyer firstname.lastname@example.org
Substantial contributions of code by: Leonhard Held, Howard Burkom, Thais Correa, Mathias Hofmann, Christian Lang, Juliane Manitz, Andrea Riebler, Daniel Sabanes Bove, Maelle Salmon, Dirk Schumacher, Stefan Steiner, Mikko Virtanen, Wei Wei, Valentin Wimmer .
Furthermore, the authors would like to thank the following people for ideas, discussions, testing and feedback: Doris Altmann, Johannes Bracher, Caterina De Bacco, Johannes Dreesman, Johannes Elias, Marc Geilhufe, Jim Hester, Kurt Hornik, Mayeul Kauffmann, Junyi Lu, Lore Merdrignac, Tim Pollington, Marcos Prates, André Victor Ribeiro Amaral, Brian D. Ripley, François Rousseu, Barry Rowlingson, Christopher W. Ryan, Klaus Stark, Yann Le Strat, André Michael Toschke, Wei Wei, George Wood, Achim Zeileis, Bing Zhang .
citation(package="surveillance") gives the two main software
references for the modeling (Meyer et al., 2017) and the monitoring
(Salmon et al., 2016) functionalities:
Meyer S, Held L, 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 .
Salmon M, Schumacher D, Höhle M (2016). “Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance.” Journal of Statistical Software, 70(10), 1--35. doi:10.18637/jss.v070.i10 .
Further references are listed in
If you use the surveillance package in your own work, please do cite the corresponding publications.