Bayesian Modeling of the Temporal Dynamics of COVID-19 using PyMC3

Data+AI Summit, Europe 2020

These are the slides for the talk given in the Data Science Lounge at the Data+AI Summit, 2020. Introduction This post is a demonstration of how to use PyMC3 to infer the disease parameters for COVID-19. PyMC3 is a probablistic programming framework that is used for Bayesian modeling. It accomplishes this through both Markov Chain Monte Carlo (MCMC) and Variational Inference methods. The work here looks at using the currently available data for the infected cases in the United States as a time-series and attempts to model this using a compartmental model. [Read More]