Unraveling the dynamic importance of county-level features in trajectory of COVID-19.

ABSTRACT: 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 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categoriz

SUBMITTER: Li Q 

PROVIDER: S-EPMC8219723 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature