surveillance (version 1.4-2)
Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic
Phenomena
Description
A package implementing statistical methods for the
modeling and change-point detection in time series of counts,
proportions and categorical data, as well as for the modeling
of continuous-time epidemic phenomena, e.g. discrete-space
setups such as the spatially enriched
Susceptible-Exposed-Infectious-Recovered (SEIR) models for
surveillance data, or continuous-space point process data such
as the occurrence of disease or earthquakes. Main focus is on
outbreak detection in count data time series originating from
public health surveillance of infectious diseases, but
applications could just as well originate from environmetrics,
reliability engineering, econometrics or social sciences.
Currently the package contains implementations of 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, negative binomial CUSUM
methods and a detector based on generalized likelihood ratios.
Furthermore, inference methods for the retrospective infectious
disease model in Held et al (2005), Held et al (2006), Paul et
al (2008) and Paul and Held (2011) are provided. A novel CUSUM
approach combining logistic and multinomial logistic modelling
is also included. Continuous self-exciting spatio-temporal
point processes are modeled through additive-multiplicative
conditional intensities as described in H�hle (2009)
("twinSIR", discrete space) and Meyer et al (2012) ("twinstim",
continuous space). 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.