Adding confidence intervals and stochastic effects to your CoVID-19 Models

This is the third post of the “Epidemic Modeling” series. We will be building up on our discussion from the first two posts:

• Epidemic Modeling 101: Or why your CoVID-19 exponential fits are wrong

• Epidemic Modeling 102: All CoVID-19 models are wrong, but some are useful

so you might want to start reading there.

The next post in the series has already been published, and you might want to check it out:

• Epidemic Modeling 104: Impact of Seasonal effects on CoVID-19

The jupyter notebooks I wrote to implement the models and generate the figures can be found over at the GitHub repository dedicated to this series:

What follows is my personal perspective, as an individual with some real world experience in epidemic modeling during previous pandemics and shouldn’t reflect on any group or institution I might be affiliated with.

So, without further ado…