Surveillance

R package surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena: The intention of the R-package surveillance is to provide open source software for the temporal and spatio-temporal visualization, modelling and monitoring of epidemic phenomena. This includes count, binary and categorical data time series as well as continuous-time processes having discrete or continuous spatial resolution.


References in zbMATH (referenced in 23 articles , 3 standard articles )

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  1. Chris Groendyke; David Welch: epinet: An R Package to Analyze Epidemics Spread across Contact Networks (2018)
  2. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018)
  3. Samuel Jenness; Steven Goodreau; Martina Morris: EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks (2018)
  4. Sebastian Meyer and Leonhard Held and Michael Höhle: Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance (2017)
  5. Veloso, Bráulio M.; Correa, Thais R.; Prates, Marcos O.; Oliveira, Gabriel F.; Tavares, Andréa I.: \itMAD-STEC: a method for multiple automatic detection of space-time emerging clusters (2017)
  6. Wei, Wei; Balabdaoui, Fadoua; Held, Leonhard: Calibration tests for multivariate Gaussian forecasts (2017)
  7. Maëlle Salmon; Dirk Schumacher; Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance (2016)
  8. Paiva, Thais; Assunção, Renato; Simões, Taynãna: Prospective space-time surveillance with cumulative surfaces for geographical identification of the emerging cluster (2015)
  9. Salmon, Maëlle; Schumacher, Dirk; Stark, Klaus; Höhle, Michael: Bayesian outbreak detection in the presence of reporting delays (2015)
  10. Höhle, Michael; an der Heiden, Matthias: Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011 (2014)
  11. Meyer, Sebastian; Held, Leonhard: Power-law models for infectious disease spread (2014)
  12. Manitz, Juliane; Höhle, Michael: Bayesian outbreak detection algorithm for monitoring reported cases of campylobacteriosis in Germany (2013)
  13. Mehmood, Rashid; Riaz, Muhammad; Does, Ronald J. M. M.: Efficient power computation for $r$ out of $m$ runs rules schemes (2013)
  14. Edzer Pebesma: spacetime: Spatio-Temporal Data in R (2012)
  15. Held, Leonhard; Paul, Michaela: Modeling seasonality in space-time infectious disease surveillance data (2012)
  16. Meyer, Sebastian; Elias, Johannes; Höhle, Michael: A space-time conditional intensity model for invasive meningococcal disease occurrence (2012)
  17. Schrödle, Birgit; Held, Leonhard; Rue, Håvard: Assessing the impact of a movement network on the spatiotemporal spread of infectious diseases (2012)
  18. Choi, Byeong Yeob; Kim, Ho; Go, Un Yeong; Jeong, Jong-Hyeon; Lee, Jae Won: Comparison of various statistical methods for detecting disease outbreaks (2010)
  19. Assunção, Renato; Correa, Thais: Surveillance to detect emerging space-time clusters (2009)
  20. Höhle, Michael; Paul, Michaela: Count data regression charts for the monitoring of surveillance time series (2008)

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