Estimation of epidemiological parameters with 
 Laplacian-P-splines following the methodology of Gressani et al. (2022)
 <doi:10.1371/journal.pcbi.1010618>.

Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) <https://www.springer.com/gp/book/9783319974866>. The package contains functions to study the S(E)IR model, spatial and age-structured 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 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) <doi:10.12688/wellcomeopenres.16006.1> and
    Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>.