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# epidemics: A library of compartmental epidemic scenario models <img src="man/figures/logo.svg" align="right" width="130"/>
# epidemics: Composable epidemic scenario modelling <img src="man/figures/logo.svg" align="right" width="130"/>

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status](https://www.r-pkg.org/badges/version/epidemics)](https://CRAN.R-project.org/package=epidemics)
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*epidemics* is an R package that provides a convenient interface to a
library of compartmental models that can help to model epidemic
scenarios for directly transmitted respiratory infections such as
influenza or Covid-19 as well haemorrhagic fevers such as Ebola virus
disease.
*epidemics* is an R package that provides modular representations of
populations and public health response measures, allowing them to be
combined with epidemiological model structures curated from the
published literature, to conveniently compose and compare epidemic
scenario models.

The models in *epidemics* implement methods outlined in Bjørnstad et al.
The models in *epidemics* focus on directly transmitted infections, and
implement methods outlined in Bjørnstad et al.
([2020a](#ref-bjornstad2020a)) and Bjørnstad et al.
([2020b](#ref-bjornstad2020)). The models in *epidemics* can help
provide rough estimates of the course of epidemics, and the
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## Package models

*epidemics* currently provides three models:
*epidemics* provides a convenient interface to a library of
compartmental models that can help to model epidemic scenarios for
directly transmitted respiratory infections such as influenza or
Covid-19 as well haemorrhagic fevers such as Ebola virus disease:

1. A deterministic SEIR-V model with susceptible, exposed, infectious,
recovered, and vaccinated compartments (SEIR-V), allowing for
heterogeneity in social contacts, the implementation of a
group-specific non-pharmaceutical intervention that reduces social
contacts, and a vaccination regime with group-specific start and end
dates,
dates;

2. The deterministic Vacamole model developed at [RIVM, the Dutch
Public Health Institute](https://www.rivm.nl/) for the Covid-19
pandemic, with a focus on scenario modelling for hospitalisation and
vaccination ([Ainslie et al. 2022](#ref-ainslie2022)),
vaccination ([Ainslie et al. 2022](#ref-ainslie2022));

3. A stochastic, discrete-time, compartmental SEIR model suitable for
modelling haemorrhagic fevers such as Ebola Virus Disease, including
hospitalisation and hospital and funeral transmissions, adapted from
Li et al. ([2019](#ref-li2019)) and Getz and Dougherty
([2018](#ref-getz2018)),
([2018](#ref-getz2018));

4. An initial implementation of a compartmental model for diphtheria in
the context of internally displaced persons camps, including a
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