# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "surveillance" in publications use:'
type: software
license: GPL-2.0-only
title: 'surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic
  Phenomena'
version: 1.24.0
doi: 10.18637/jss.v070.i10
identifiers:
- type: doi
  value: 10.32614/CRAN.package.surveillance
abstract: 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) <https://doi.org/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) <https://doi.org/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) <https://doi.org/10.1002/sim.4177> and Meyer and Held (2014) <https://doi.org/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) <https://doi.org/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) <https://doi.org/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) <https://doi.org/10.18637/jss.v077.i11>.
authors:
- family-names: Hoehle
  given-names: Michael
  orcid: https://orcid.org/0000-0002-0423-6702
- family-names: Meyer
  given-names: Sebastian
  email: seb.meyer@fau.de
  orcid: https://orcid.org/0000-0002-1791-9449
- family-names: Paul
  given-names: Michaela
preferred-citation:
  type: article
  title: 'Monitoring Count Time Series in R: Aberration Detection in Public Health
    Surveillance'
  authors:
  - family-names: Salmon
    given-names: Maëlle
  - family-names: Schumacher
    given-names: Dirk
  - family-names: Höhle
    given-names: Michael
  journal: Journal of Statistical Software
  year: '2016'
  volume: '70'
  issue: '10'
  doi: 10.18637/jss.v070.i10
  start: '1'
  end: '35'
repository: https://epiverse-connect.r-universe.dev
commit: 4bb665ac4ae9935cc968f3b4a10230cd29cfbf7b
url: https://surveillance.R-Forge.R-project.org/
date-released: '2024-10-01'
contact:
- family-names: Meyer
  given-names: Sebastian
  email: seb.meyer@fau.de
  orcid: https://orcid.org/0000-0002-1791-9449
references:
- type: article
  title: Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance
  authors:
  - family-names: Meyer
    given-names: Sebastian
  - family-names: Held
    given-names: Leonhard
  - family-names: Höhle
    given-names: Michael
  journal: Journal of Statistical Software
  year: '2017'
  volume: '77'
  issue: '11'
  doi: 10.18637/jss.v077.i11
  start: '1'
  end: '55'