A walkthrough of how SIR infectious disease modeling works, along with a do-it-yourself Python model that you can use to simulate a COVID lockdown.
If you work through the videos and the notebooks you'll end up with a pretty solid foundational understanding of how COVID models work. You'll also (hopefully) understand how small changes in concepts like "how many people does an average person interact with each day?" can create a large difference in how many people end up infected.
Disclaimer: I don't know anything about epidemiology! like all the other experts on Twitter, I just started looking into this stuff v recently.
But hopefully this material can do a half-legitimate job of explaining some of the concepts behind a simple model and how everything fits together.
You'll want to check out the YouTube playlist, but hey, I've broken it down here anyway:
- What is disease modeling? Intro to the SIR model.
- Contact rate and removal rate in the SIR model of infectious disease
- Breaking down R0 in epidemic models
- Building a COVID disease model in Python (with shelter in place)
You need the videos. Just looking at the notebooks would be a crime.
Me scrawling down words and phrases as I work through the videos above. Not terribly interesting, and you can get all of its content out of watching the video.
A do-it-yourself SIR model where you can put in different effective contact rates and removal rates and see how things shake out.
Includes some homework problems!
PyRoss, a Python library that implements SIR, SEIR, SEAIR, and SEAIRQ models.
Epidemic Calculator, an interactive tool that you can now understand a lot better!