The interplay of demographic variables and social distancing scores in deep prediction of US covid-19 cases
The
interplay of demographic variables and social distancing scores in deep
prediction of US covid-19 cases Francesca Tang, Yang Feng, Hamza Chiheb, Jianqing Fan Keywords: Community Detection, COVID-19, Machine Learning, Neural Networks, Spectral Clustering |
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.