surveillance

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) doi:10.1016/j.csda.2008.02.015. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) doi:10.18637/jss.v070.i10. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) doi:10.1002/sim.4177 and Meyer and Held (2014) doi:10.1214/14-AOAS743. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) doi:10.1002/bimj.200900050. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) doi:10.1111/j.1541-0420.2011.01684.x. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) doi:10.18637/jss.v077.i11.

Help pages

175 (11 internal)includes a package overview page

Examples that run

98%126 run in a plain check (5 of them under a condition) · 3 never run

Exported functions with examples

77%

Documented parameters

86%

Return-value docs

84%

Help pages citing references

10% with a bibliography file

Also includes 4 demo scripts (run with demo())

Installing surveillance adds 8 packages (7 with compiled code, 8.5 MB of source tarballs). Heaviest: sp 4.3 MB, spatstat.data 2.9 MB, spatstat.geom 816 KB.

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First published

2005-11-18

Total releases

91 / 21 yrs

License

GPL-2 OSI

Additional repositories

inla.r-inla-download.org

Minimum R

≥ 3.6.0

Bundled data

812 KB / 41 files

Download size

3.7 MB

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The package provides a citation file, so citation() returns the authors' own entry.

It points to 10.18637/jss.v070.i10, 10.18637/jss.v077.i11.

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