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

Autor:	Cong, Lin William, Tang, Ke, Wang, Bing, Wang, Jingyuan
Rok vydání:	2021
Předmět:	Economics - General Economics Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
Popis:	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. Comment: Preprint, not peer reviewed
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.10009 Zobrazit plný text záznamu View this record from Arxiv