  The objective of this study was to investigate the importance of multiple
county-level features in the trajectory of COVID-19. We examined feature
importance across 2,787 counties in the United States using a data-driven
machine learning model. We trained random forest models using 23 features
representing six key influencing factors affecting pandemic spread: social
demographics of counties, population activities, mobility within the counties,
movement across counties, disease attributes, and social network structure.
Also, we categorized counties into multiple groups according to their
population densities, and we divided the trajectory of COVID-19 into three
stages: the outbreak stage, the social distancing stage, and the reopening
stage. The study aims to answer two research questions: (1) The extent to which
the importance of heterogeneous features evolves in different stages; (2) The
extent to which the importance of heterogeneous features varies across counties
with different characteristics. We fitted a set of random forest models to
determine weekly feature importance. The results showed that: (1) Social
demographic features, such as gross domestic product, population density, and
minority status maintained high-importance features throughout stages of
COVID-19 across the 2787 studied counties; (2) Within-county mobility features
had the highest importance in county clusters with higher population densities;
(3) The feature reflecting the social network structure (Facebook, social
connectedness index), had higher importance in the models for counties with
higher population densities. The results show that the data-driven machine
learning models could provide important insights to inform policymakers
regarding feature importance for counties with various population densities and
in different stages of a pandemic life cycle.
