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

Lin William Cong, Ke Tang, Bing Wang, and Jingyuan Wang

Available at SSRN 3901449, 2021Download


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.

An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States
An AI-assisted Economic Model of Endogen
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@article{cong2021ai,

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

  author={Cong, Lin William and Tang, Ke and Wang, Bing and Wang, Jingyuan},

  journal={CoRR},

  volume={abs/2109.10009},

  year={2021}

}