Package: epinowcast 0.6.0.1000

epinowcast: A Bayesian Framework for Real-time Infectious Disease Surveillance

A modular Bayesian framework for real-time infectious disease surveillance. Provides tools for nowcasting, reproduction number estimation, delay estimation, and forecasting from data subject to reporting delays, right-truncation, missing data, and incomplete ascertainment. Users can build models suited to their setting using a flexible formula interface supporting fixed effects, random effects, random walks, and time-varying parameters, with options including parametric and non-parametric delay distributions with optional modifiers (via discrete-time hazard models), renewal processes, observation models, missing data imputation, and stratified analyses with partial pooling. By jointly estimating disease dynamics and reporting patterns, our framework enables earlier and more reliable detection of trends. While designed with epidemiological applications in mind, the framework can be applied to any right-truncated time series count data.

Authors:Sam Abbott [aut, cre], Adrian Lison [aut], Sebastian Funk [aut], Carl Pearson [aut], Hugo Gruson [aut], Felix Guenther [aut], Michael DeWitt [aut], James Mba Azam [aut], Jessalyn Sebastian [aut], Hannah Choi [ctb], Pratik Gupte [ctb], Joel Hellewell [ctb], Luis Rivas [ctb], Sang Woo Park [ctb], Nathan McIntosh [ctb], Kath Sherratt [ctb], Nikos Bosse [ctb], Adam Howes [ctb], Kaitlyn Johnson [ctb], Barbora Nemcova [ctb]