# EpiModel

### From HPMRG

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Next read the lecture notes | Next read the lecture notes | ||

- | [https://drive.google.com/ | + | [https://drive.google.com/open?id=1G7Pf3S5hxnZeqrZb9ZKAxayfoEXPNZvD 202Clecture0 (Part 1)], |

<i>Modeling the effects of public health interventions on COVID-19 transmission</i> which are adapted notes from | <i>Modeling the effects of public health interventions on COVID-19 transmission</i> which are adapted notes from | ||

[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]. | [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 [http://statnet.org/nme/d1-s2.pdf Deterministic Compartmental Models]. This is the <u>discrete time</u> version of the SIR model from the lecture notes [https://drive.google.com/ | + | Next, work through the tutorial [http://statnet.org/nme/d1-s2.pdf Deterministic Compartmental Models]. This is the <u>discrete time</u> version of the SIR model from the lecture notes [https://drive.google.com/open?id=1G7Pf3S5hxnZeqrZb9ZKAxayfoEXPNZvD 202Clecture0 (Part 1)]. |

Next, work through the tutorial [http://statnet.org/tut/BasicDCMs.html Basic DCMs with EpiModel] | Next, work through the tutorial [http://statnet.org/tut/BasicDCMs.html Basic DCMs with EpiModel] | ||

and through the tutorial [http://statnet.org/tut/BasicICMs.html Basic ICMs with EpiModel] | and through the tutorial [http://statnet.org/tut/BasicICMs.html Basic ICMs with EpiModel] | ||

- | + | Next, work through the lecture notes | |

- | [https://drive.google.com/ | + | [https://drive.google.com/open?id=1Y7jb3_RDv2X4baFmRuRhuQ-brtqaD1Pi 202Clecture0 (Part 2)], |

which are also adapted notes from | which are also adapted notes from | ||

[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]. | |||

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

+ | |||

+ | The next lecture notes cover some possible interventions. To see these work through | ||

+ | [https://drive.google.com/open?id=19Ju6J1bumUgtGRyMk0gRdJ6K7PJsY9N6 202Clecture0 (Part 3)] and | ||

+ | [https://drive.google.com/open?id=1Q15tlE7h_rgdBPVHpTY-dSmlRczLDuYe 202Clecture0 (Part 4)]. | ||

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

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<h2><strong> Lecture notes (PDF) </strong></h2> | <h2><strong> Lecture notes (PDF) </strong></h2> | ||

{| | {| | ||

- | |[https://drive.google.com/ | + | |[https://drive.google.com/open?id=1G7Pf3S5hxnZeqrZb9ZKAxayfoEXPNZvD Lecture 0 (Part 1) (PDF)] |

|- | |- | ||

- | |[https://drive.google.com/ | + | |[https://drive.google.com/open?id=1Y7jb3_RDv2X4baFmRuRhuQ-brtqaD1Pi Lecture 0 (Part 2) (PDF)] |

|- | |- | ||

- | |[https://drive.google.com/ | + | |[https://drive.google.com/open?id=19Ju6J1bumUgtGRyMk0gRdJ6K7PJsY9N6 Lecture 0 (Part 3) (PDF)] |

|- | |- | ||

- | |[https://drive.google.com/ | + | |[https://drive.google.com/open?id=1Q15tlE7h_rgdBPVHpTY-dSmlRczLDuYe Lecture 0 (Part 4) (PDF)] |

|} | |} | ||

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|[https://drive.google.com/file/d/1hTVfW7Hl90KNrEhPeNK-p1It4aQc4snb/view?usp=sharing Lecture 0 (Part 3)(Rmd)] | |[https://drive.google.com/file/d/1hTVfW7Hl90KNrEhPeNK-p1It4aQc4snb/view?usp=sharing Lecture 0 (Part 3)(Rmd)] | ||

|- | |- | ||

- | |[https://drive.google.com/file/d/ | + | |[https://drive.google.com/file/d/1S2eVQLFVSGei0j2IYCBNDmtdXP_QAC4g/view?usp=sharing Lecture 0 (Part 4)(Rmd)] |

|} | |} |

## Current revision as of 06:59, 3 April 2020

## 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
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 202Clecture0 (Part 1).

Next, work through the tutorial Basic DCMs with EpiModel and through the tutorial Basic ICMs with EpiModel

Next, 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.

The next lecture notes cover some possible interventions. To see these work through 202Clecture0 (Part 3) and 202Clecture0 (Part 4).

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

## ** Lecture notes (PDF) **

Lecture 0 (Part 1) (PDF) |

Lecture 0 (Part 2) (PDF) |

Lecture 0 (Part 3) (PDF) |

Lecture 0 (Part 4) (PDF) |

## ** Videos of Lectures **

## ** Lecture notes (source code as Rmarkdown) **

Lecture 0 (Part 1)(Rmd) |

Lecture 0 (Part 2)(Rmd) |

Lecture 0 (Part 3)(Rmd) |

Lecture 0 (Part 4)(Rmd) |