#Covid

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]

This Jupyter notebook contains disease propagation modeling using the SIR, SIS and SIRS models in Julia. The impact of social distancingis assessed using these various models. Also shown is the outbreak size as a function of the mean degree of connectivity (physical interaction) of the social network.

Jupyter notebook on Github