  We demonstrate an approach to replicate and forecast the spread of the
SARS-CoV-2 (COVID-19) pandemic using the toolkit of probabilistic programming
languages (PPLs). Our goal is to study the impact of various modeling
assumptions and motivate policy interventions enacted to limit the spread of
infectious diseases. Using existing compartmental models we show how to use
inference in PPLs to obtain posterior estimates for disease parameters. We
improve popular existing models to reflect practical considerations such as the
under-reporting of the true number of COVID-19 cases and motivate the need to
model policy interventions for real-world data. We design an SEI3RD model as a
reusable template and demonstrate its flexibility in comparison to other
models. We also provide a greedy algorithm that selects the optimal series of
policy interventions that are likely to control the infected population subject
to provided constraints. We work within a simple, modular, and reproducible
framework to enable immediate cross-domain access to the state-of-the-art in
probabilistic inference with emphasis on policy interventions. We are not
epidemiologists; the sole aim of this study is to serve as an exposition of
methods, not to directly infer the real-world impact of policy-making for
COVID-19.

Machine Learning;Machine Learning;Populations and Evolution