Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuoustime 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 GLRCUSUM method of Hhle 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 realworld data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatiotemporal 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 endemicepidemic 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:10.1214/14AOAS743>. twinSIR() models the susceptibleinfectiousrecovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hhle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates selfexciting point process models for a spatiotemporal point pattern of infective events, e.g., timestamped georeferenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.15410420.2011.01684.x>. A recent overview of the implemented spacetime modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.
Package details 


Author  Michael Hhle [aut, ths] (<https://orcid.org/0000000204236702>), Sebastian Meyer [aut, cre] (<https://orcid.org/0000000217919449>), Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Sabans Bov [ctb], Malle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R) 
Maintainer  Sebastian Meyer <seb.meyer@fau.de> 
License  GPL2 
Version  1.19.1 
URL  https://surveillance.RForge.Rproject.org/ 
Package repository  View on CRAN 
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