Using Bayesian Hierarchical Models to Infer the Disease Parameters of COVID-19

In a previous post (https://lnkd.in/dZvmsRm), I looked at the available data for the infected cases in the United States as a time-series, modeling this as a compartmental probabilistic model and inferring the disease parameters such as R0 using Bayesian estimation. However, we can use the case counts from several countries and use Bayesian hierarchical models to extend this work and better estimate R0. In this post I illustrate how we can do exactly that using PyMC3.

Blog post

If you would like to learn more about Bayesian inference and using PyMC3, please check out our new Coursera courses:

1. Bayesian Inference with MCMC - https://lnkd.in/d6cZFYN
2. Introduction to PyMC3 for Bayesian Modeling and Inference -https://lnkd.in/d_XNKb2