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

TL;DR

This study combines spectral clustering, demographic analysis, and LSTM prediction to understand and forecast COVID-19 case growth in U.S. counties, aiding targeted resource allocation.

Contribution

It introduces a novel approach integrating spectral clustering, demographic features, and social distancing scores for detailed county-level COVID-19 growth prediction.

Findings

Identified key demographic factors influencing growth

Successfully predicted future case trajectories using social distancing data

Mapped counties into growth communities for targeted interventions

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.