Work

The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID-19 Cases

Year: 2021

Type: preprint

Abstract: With the severity of the COVID-19 outbreak, we characterize the nature of the growth trajectories of counties in the United States using a novel combination of spectral clustering and the correlation matrix. As the U.S. and the rest of the world are experiencing a severe second wave of infections, the importance of assigning growth membership to counties and understanding the determinants of the growth are increasingly evident. Subsequently, we select the demographic features that are most statistically significant in distinguishing the communities. Lastly, we effectively predict the future growth of a given county with an LSTM using three social distancing scores. This comprehensive study captures the nature of counties' growth in cases at a very micro-level using growth communities, demographic factors, and social distancing performance to help government agencies utilize known information to make appropriate decisions regarding which potential counties to target resources and funding to.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2101.02113

Authors: Tang, Francesca, Feng, Yang, Chiheb, Hamza, Fan, Jianqing

Institutions: Princeton University, New York University

Language: English

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health

Awards: CAREER: Statistical inference of network and relational data, Robust and Distributed Statistical Learning from Big Data, Collaborative Research: RAPID: Behavioral Epidemic Modeling For COVID-19 Containment

OpenAlex ID: W3121097837

Cites: 34

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Related to: 10