surveillance (version 1.10-0)
Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic
Phenomena
Description
Implementation of 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,
or continuous-space point process data such as the
occurrence of infectious diseases. Main focus is on outbreak
detection in count data time series originating from public health
surveillance of communicable diseases, but applications could just as well
originate from environmetrics, reliability engineering, econometrics or
social sciences.
Currently, the package contains implementations of many typical
outbreak detection procedures such as Farrington et al (1996),
Noufaily et al (2012) or the negative binomial LR-CUSUM method
described in H�hle and Paul (2008). A novel CUSUM approach combining
logistic and multinomial logistic modelling is also included.
Furthermore, inference methods for the retrospective infectious
disease models in Held et al (2005), Held et al (2006),
Paul et al (2008), Paul and Held (2011), Held and Paul (2012),
and Meyer and Held (2014) are provided.
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: Using the 'boda' algorithm requires the 'INLA'
package, which should be installed automatically through the
specified Additional_repositories, if uninstalled dependencies
are also requested. As this might not work under OS X it
might be necessary to manually install the 'INLA' package as
specified at .