# EpiModel

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Lecture notes (PDF)

Lecture notes (PDF)

Lecture notes (PDF)

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## Contents

### Introduction to COVID-19 Epidemic modeling via EpiModel

These notes are a simple introduction to

In these notes we develop mathematical and stochastic models of the local epidemic spread of COVID- 19 infection. These stochastic compartmental models are implemented by the EpiModel software package. EpiModel is built on the R statistical language. In these notes we will examine the effect of various public health interventions on the epidemic spread.

## Suggested course

I suggest you first view the first video.

These follow the tutorial Introduction to Network Modeling. It is a quite an introductory level and so you can work through it quickly, but it get some basic concepts across.

Next read the lecture notes 202Clecture0 (Part 1), ``Modeling the effects of public health interventions on COVID-19 transmission" which are adapted notes from [\href{]https://timchurches.github.io/blog/posts/2020-03-10-modelling-the-effects-of-public-health-interventions-on-covid-19-transmission-part-1 work by Tim Churches[].

Next, work through the tutorial Deterministic Compartmental Models. This is the discrete time version of the SIR model from the lecture notes [https://drive.google.com/file/d/1_X0eH_apuhW0qby2WAxvUEjCDevG1EAt/view?usp=sharing 202Clecture0}.

Next, work through the tutorial \href{http://statnet.org/tut/BasicDCMs.html}{Basic DCMs with EpiModel} and through the tutorial \href{http://statnet.org/tut/BasicICMs.html}{Basic ICMs with EpiModel}

Finally, work through the lecture notes 202Clecture0 (Part 2), which are also adapted notes from work by Tim Churches.

The PDF are narrated in the video so you can view the videos after reading the PDF notes.

If you want to run the models yourself, use the Rmarkdown (Rmd) files at the end. They are best run in Rstudio.