surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

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 Farrington et al (1996), Noufaily et al (2012) or the negative binomial LR-CUSUM method described in Hoehle and Paul (2008). 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 Höhle (2009) ("twinSIR", discrete space) and Meyer et al (2012) ("twinstim", continuous space). The package contains several real-world data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion. Note: The suggested package INLA is unfortunately not available from any mainstream repository - in case one wants to use the 'boda' algorithm one needs to manually install the INLA package as specified at http://www.r-inla.org/download.

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