surveillance (version 1.2-1)
Modeling and monitoring discrete response time series
Description
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.