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arXiv · 2110.12461

Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes

James A. Scott·Axel Gandy·Swapnil Mishra·Samir Bhatt·Seth Flaxman·H. Juliette T. Unwin·Jonathan Ish-Horowicz
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Abstract

This article introduces epidemia, an R package for Bayesian, regression-oriented modeling of infectious diseases. The implemented models define a likelihood for all observed data while also explicitly modeling transmission dynamics: an approach often termed as semi-mechanistic. Infections are propagated over time using renewal equations. This approach is inspired by self-exciting, continuous-time point processes such as the Hawkes process. A variety of inferential tasks can be performed using the package. Key epidemiological quantities, including reproduction numbers and latent infections, may be estimated within the framework. The models may be used to evaluate the determinants of changes in transmission rates, including the effects of control measures. Epidemic dynamics may be simulated either from a fitted model or a prior model; allowing for prior/posterior predictive checks, experimentation, and forecasting.

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James A. Scott, Axel Gandy, Swapnil Mishra, Samir Bhatt, Seth Flaxman, H. Juliette T. Unwin, Jonathan Ish-Horowicz. 2021-10-24. Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes. https://arxiv.org/abs/2110.12461

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