Functions, data sets and shiny apps for "Epidemics: Models and Data in R (2nd edition)" by Ottar N. Bjornstad (2022, ISBN: 978-3-031-12055-8) <https://link.springer.com/book/10.1007/978-3-319-97487-3>. The package contains functions to study the Susceptible-Exposed-Infected-Removed SEIR model, spatial and age-structured Susceptible-Infected-Removed SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the 'Coursera' Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.

Tools for simulating mathematical models of infectious disease dynamics.
    Epidemic model classes include deterministic compartmental models, stochastic
    individual-contact 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 API for
    extending these templates to address novel scientific research aims. Full
    methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).

A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.

Allows for forward-in-time simulation of epistatic networks with associated
    phenotypic output.

Estimates the time-varying reproduction number,
    rate of spread, and doubling time using a range of open-source tools
    (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and
    current best practices (Gostic et al. (2020)
    <doi:10.1101/2020.06.18.20134858>).  It aims to help users avoid some
    of the limitations of naive implementations in a framework that is
    informed by community feedback and is actively supported.