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surveillance (version 1.6-0)
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.