Spatial prediction of COVID-19 pandemic dynamics in the United States.

ABSTRACT: The impact of COVID-19 across the United States has been heterogeneous, with rapid spread and greater mortality in some areas compared with others. We used geographically-linked data to test the hypothesis that the risk for COVID-19 is defined by location and sought to define which demographic features are most closely associated with elevated COVID-19 spread and mortality. We leveraged geographically-restricted social, economic, political, and demographic information from US counties, to develop a computational framework using structured Gaussian processing to predict county-level case and death counts during the pandemic's initial and nationwide phases. After identifying the most predictive information sources by location, we applied an unsupervised clustering algorithm and topic modelli

SUBMITTER: Ak C 

PROVIDER: S-EPMC12674611 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature