surveillance (version 1.8-1)
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 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 H�hle (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.