An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States

TL;DR

This paper develops an AI-enhanced epidemiological-economic model for COVID-19 in the US, integrating mobility, health, and economic data to analyze disease spread and policy impacts.

Contribution

It introduces a novel deep-learning-based SEIR-AIM model linking mobility, economic activity, and health data for COVID-19 analysis.

Findings

Reproduction number stabilizes around one before vaccination.

Reopening schools and workplaces are most effective policies.

Black Lives Matter protests have negligible public health impact.

Abstract

We build a deep-learning-based SEIR-AIM model integrating the classical Susceptible-Exposed-Infectious-Removed epidemiology model with forecast modules of infection, community mobility, and unemployment. Through linking Google's multi-dimensional mobility index to economic activities, public health status, and mitigation policies, our AI-assisted model captures the populace's endogenous response to economic incentives and health risks. In addition to being an effective predictive tool, our analyses reveal that the long-term effective reproduction number of COVID-19 equilibrates around one before mass vaccination using data from the United States. We identify a "policy frontier" and identify reopening schools and workplaces to be the most effective. We also quantify protestors' employment-value-equivalence of the Black Lives Matter movement and find that its public health impact to be negligible.