blog VS Epidemiology101

Teddy Koker did a blog some time back about creating a survivorship bias-free dataset and used an S&P 500 ETF as the analogue; his code (might be that link or a different one under his github) showed how to scrape (recent) historical holdings of that particular ETF IIRC. As one potential data source.

Trying to put the finishing touches on a Python package [1] for epidemic modeling using Compartmental models. This grew out of a series of blog posts I started writing during the pandemic [2] based on my professional experience in epidemic modeling in a previous life where I was the lead developer for a state of the art global epidemic model [3].

[1] http://github.com/DataForScience/epidemik

[2] https://github.com/DataForScience/Epidemiology101

[3] https://www.gleamviz.org/explore.html

No worries, Google does tend to do a good job of monopolizing attention in whatever they do and Epidemic Modeling is... complicated. Probably much more complicated than pretty much any other kind of modeling since people have the bad habit of thinking and acting in whatever way they want (sometimes with the explicit purpose of breaking your model :).

Now, if you want to see the real-world state-of-the-art epidemic modeling on a global scale, checkout GLEaM/GLEaMViz https://www.gleamviz.org/ (full disclaimer, in a previous life I was the lead developer).

And if you're interested in a basic intro, you can also checkout my (somewhat neglected) series of blog posts from the pandemic days: https://github.com/DataForScience/Epidemiology101