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 35 articles , 3 standard articles )

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  1. Gallagher, Shannon K.; Follmann, Dean: Branching process models to identify risk factors for infectious disease transmission (2022)
  2. Hahn, Georg: Online multivariate changepoint detection with type I error control and constant time/memory updates per series (2022)
  3. Manjoo-Docrat, Raeesa: A spatio-stochastic model for the spread of infectious diseases (2022)
  4. Abolhassani, Ali; Prates, Marcos O.: An up-to-date review of scan statistics (2021)
  5. Adelfio, Giada; Chiodi, Marcello: Including covariates in a space-time point process with application to seismicity (2021)
  6. James A. Scott, Axel Gandy, Swapnil Mishra, Samir Bhatt, Seth Flaxman, H. Juliette T. Unwin, Jonathan Ish-Horowicz: Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes (2021) arXiv
  7. Rodríguez-Berrio, Felipe; Rodríguez-Cortés, Francisco J.; Mateu, Jorge; Adelfio, Giada: On some statistical properties of the spatio-temporal product density (2021)
  8. Waleed Almutiry, Vineetha Warriyar K V, Rob Deardon: Continuous Time Individual-Level Models of Infectious Disease: a Package EpiILMCT (2020) arXiv
  9. Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes: DRHotNet: An R package for detecting differential risk hotspots on a linear network (2019) arXiv
  10. Virgilio Gómez-Rubio; Paula Moraga; John Molitor; Barry Rowlingson: DClusterm: Model-Based Detection of Disease Clusters (2019) not zbMATH
  11. Brelsford, Christa; De Bacco, Caterina: Are `water smart landscapes’ contagious? An epidemic approach on networks to study peer effects (2018)
  12. Chris Groendyke; David Welch: epinet: An R Package to Analyze Epidemics Spread across Contact Networks (2018) not zbMATH
  13. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  14. Meyer, Sebastian: Self-exciting point processes: infections and implementations (2018)
  15. Samuel Jenness; Steven Goodreau; Martina Morris: EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks (2018) not zbMATH
  16. Sebastian Meyer and Leonhard Held and Michael Höhle: Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance (2017) not zbMATH
  17. 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)
  18. Wei, Wei; Balabdaoui, Fadoua; Held, Leonhard: Calibration tests for multivariate Gaussian forecasts (2017)
  19. Maëlle Salmon; Dirk Schumacher; Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance (2016) not zbMATH
  20. Paiva, Thais; Assunção, Renato; Simões, Taynãna: Prospective space-time surveillance with cumulative surfaces for geographical identification of the emerging cluster (2015)

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