2021/09/21 by Lin William Cong, Cong, Lin William, Ke Tang +5
Computer Science · Economics, Econometrics and Finance · Mathematics · Social Sciences · #COVID-19 Pandemic Impacts #COVID-19 epidemiological studies #FOS: Computer and information sciences #FOS: Economics and business #General Economics (econ.GN) #Health disparities and outcomes #Social and Information Networks (cs.SI) #cs.SI #econ.GN #q-fin.EC
paper · pdf · doi:10.48550/arxiv.2109.10009
Preprint, not peer reviewed
arxiv created 2021/09/21 · openalex publication_date 2021/09/21 · arxiv updated 2021/09/22 · openalex created_date 2021/09/27 · openalex updated_date 2026/07/28
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.