surveillance: Modeling and monitoring discrete response time series

A package implementing statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data. 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 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) and Paul et al. (2008) are provided. A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. 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.

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