EpiNow2-package: EpiNow2: Estimate and Forecast Real-Time Infection Dynamics

EpiNow2-packageR Documentation

EpiNow2: Estimate and Forecast Real-Time Infection Dynamics

Description

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Estimates the time-varying reproduction number, rate of spread, and doubling time using a renewal equation approach combined with Bayesian inference via Stan. Supports Gaussian process and random walk priors for modelling changes in transmission over time. Accounts for delays between infection and observation (incubation period, reporting delays), right-truncation in recent data, day-of-week effects, and observation overdispersion. Can estimate relationships between primary and secondary outcomes (e.g., cases to hospitalisations or deaths) and forecast both. Runs across multiple regions in parallel. Based on Abbott et al. (2020) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.12688/wellcomeopenres.16006.1")} and Gostic et al. (2020) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1101/2020.06.18.20134858")}.

Author(s)

Maintainer: Sebastian Funk sebastian.funk@lshtm.ac.uk (ORCID)

Authors:

Other contributors:

See Also

Useful links:


EpiNow2 documentation built on June 17, 2026, 1:07 a.m.