EpiModel

R package EpiModel. Tools for simulating mathematical models of infectious disease. Epidemic model classes include deterministic compartmental models, stochastic agent-based models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an easy API for extending these templates to address novel scientific research aims.


References in zbMATH (referenced in 12 articles , 1 standard article )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Benjamin F. Maier: epipack: An infectious disease modeling package for Python (2021) not zbMATH
  2. 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
  3. Mathew Jacob: Eir: A Python Package for Epidemic Simulation (2021) not zbMATH
  4. Zeng, Pinhong: On the transmission of COVID-19 and its prevention and control management (2021)
  5. Baker, Evan; Challenor, Peter; Eames, Matt: Predicting the output from a stochastic computer model when a deterministic approximation is available (2020)
  6. Gonçalves, João N. C.; Rodrigues, Helena Sofia; Monteiro, M. Teresa T.: Preventing computer virus prevalence using epidemiological modeling and optimal control (2020)
  7. Joel C. Miller, Tony TIng: EoN (Epidemics on Networks): a fast, flexible Python package for simulation, analytic approximation, and analysis of epidemics on networks (2020) arXiv
  8. R. Adhikari, Austen Bolitho, Fernando Caballero, Michael E. Cates, Jakub Dolezal, Timothy Ekeh, Jules Guioth, Robert L. Jack, Julian Kappler, Lukas Kikuchi, Hideki Kobayashi, Yuting I. Li, Joseph D. Peterson, Patrick Pietzonka, Benjamin Remez, Paul B. Rohrbach, Rajesh Singh, Günther Turk: Inference, prediction and optimization of non-pharmaceutical interventions using compartment models: the PyRoss library (2020) arXiv
  9. Waleed Almutiry, Vineetha Warriyar K V, Rob Deardon: Continuous Time Individual-Level Models of Infectious Disease: a Package EpiILMCT (2020) arXiv
  10. Samuel Jenness; Steven Goodreau; Martina Morris: EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks (2018) not zbMATH
  11. Giulio Rossetti, Letizia Milli, Salvatore Rinzivillo, Alina Sirbu, Fosca Giannotti, Dino Pedreschi: NDlib: a Python Library to Model and Analyze Diffusion Processes Over Complex Networks (2017) arXiv
  12. Stefan Widgren, Pavol Bauer, Stefan Engblom: SimInf: An R package for Data-driven Stochastic Disease Spread Simulations (2016) arXiv