Responses to COVID-19 with Probabilistic Programming

TL;DR

This paper introduces a probabilistic programming framework to evaluate and compare the effectiveness and economic impact of different COVID-19 intervention strategies across multiple countries.

Contribution

It presents a novel generative simulation model that quantifies both health and economic outcomes of policies, enabling comprehensive assessment of intervention strategies.

Findings

Social distancing with contact tracing reduces transmission by 96%

Policy combination can minimize economic and human capital loss

Open-sourced framework for policy efficacy testing

Abstract

The COVID-19 pandemic left its unique mark on the 21st century as one of the most significant disasters in history, triggering governments all over the world to respond with a wide range of interventions. However, these restrictions come with a substantial price tag. It is crucial for governments to form anti-virus strategies that balance the trade-off between protecting public health and minimizing the economic cost. This work proposes a probabilistic programming method to quantify the efficiency of major non-pharmaceutical interventions. We present a generative simulation model that accounts for the economic and human capital cost of adopting such strategies, and provide an end-to-end pipeline to simulate the virus spread and the incurred loss of various policy combinations. By investigating the national response in 10 countries covering four continents, we found that social distancing coupled with contact tracing is the most successful policy, reducing the virus transmission rate by 96\% along with a 98\% reduction in economic and human capital loss. Together with experimental results, we open-sourced a framework to test the efficacy of each policy combination.