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Activity Number: 211 - Disease Prediction
Type: Contributed
Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #317669
Title: The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of US COVID-19 Cases
Author(s): Francesca Tang* and Yang Feng and Hamza Chiheb and Jianqing Fan
Companies: Princeton University and New York University and N/A and Princeton University
Keywords: COVID-19; Stochastic Block Model; Spectral Clustering; Community Detection; Neural Networks

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 still suffering from the effects of the virus, the importance of assigning growth membership to counties and understanding the determinants of the growth is 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 a long short-term memory (LSTM) recurrent neural network 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.

Authors who are presenting talks have a * after their name.

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