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 29 articles , 3 standard articles )

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  1. Rodríguez-Berrio, Felipe; Rodríguez-Cortés, Francisco J.; Mateu, Jorge; Adelfio, Giada: On some statistical properties of the spatio-temporal product density (2021)
  2. Waleed Almutiry, Vineetha Warriyar K V, Rob Deardon: Continuous Time Individual-Level Models of Infectious Disease: a Package EpiILMCT (2020) arXiv
  3. Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes: DRHotNet: An R package for detecting differential risk hotspots on a linear network (2019) arXiv
  4. Virgilio Gómez-Rubio; Paula Moraga; John Molitor; Barry Rowlingson: DClusterm: Model-Based Detection of Disease Clusters (2019) not zbMATH
  5. Brelsford, Christa; De Bacco, Caterina: Are `water smart landscapes’ contagious? An epidemic approach on networks to study peer effects (2018)
  6. Chris Groendyke; David Welch: epinet: An R Package to Analyze Epidemics Spread across Contact Networks (2018) not zbMATH
  7. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  8. Meyer, Sebastian: Self-exciting point processes: infections and implementations (2018)
  9. Samuel Jenness; Steven Goodreau; Martina Morris: EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks (2018) not zbMATH
  10. Sebastian Meyer and Leonhard Held and Michael Höhle: Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance (2017) not zbMATH
  11. Veloso, Bráulio M.; Correa, Thais R.; Prates, Marcos O.; Oliveira, Gabriel F.; Tavares, Andréa I.: \textitMAD-STEC: a method for multiple automatic detection of space-time emerging clusters (2017)
  12. Wei, Wei; Balabdaoui, Fadoua; Held, Leonhard: Calibration tests for multivariate Gaussian forecasts (2017)
  13. Maëlle Salmon; Dirk Schumacher; Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance (2016) not zbMATH
  14. Paiva, Thais; Assunção, Renato; Simões, Taynãna: Prospective space-time surveillance with cumulative surfaces for geographical identification of the emerging cluster (2015)
  15. Salmon, Maëlle; Schumacher, Dirk; Stark, Klaus; Höhle, Michael: Bayesian outbreak detection in the presence of reporting delays (2015)
  16. Höhle, Michael; an der Heiden, Matthias: Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011 (2014)
  17. Meyer, Sebastian; Held, Leonhard: Power-law models for infectious disease spread (2014)
  18. Manitz, Juliane; Höhle, Michael: Bayesian outbreak detection algorithm for monitoring reported cases of campylobacteriosis in Germany (2013)
  19. Mehmood, Rashid; Riaz, Muhammad; Does, Ronald J. M. M.: Efficient power computation for (r) out of (m) runs rules schemes (2013)
  20. Edzer Pebesma: spacetime: Spatio-Temporal Data in R (2012) not zbMATH

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