A Spatiotemporal Epidemiological Prediction Model to Inform County-Level COVID-19 Risk in the United States
Published in Harvard Data Science Review, 2020
Recommended citation: Zhou, Y., Wang, L., Zhang, L., Shi, L., Yang, K., He, J., … Song, P. (2020). A Spatiotemporal Epidemiological Prediction Model to Inform County-Level COVID-19 Risk in the United States. Harvard Data Science Review. https://doi.org/10.1162/99608f92.79e1f45e https://hdsr.mitpress.mit.edu/pub/qqg19a0r/release/2
Abstract: As the COVID-19 pandemic continues worsening in the United States, it is of critical importance to develop a health information system that provides timely risk evaluation and prediction of the COVID-19 infection in communities. We propose a spatiotemporal epidemiological forecast model that combines a spatial cellular automata (CA) with a temporal extended susceptible-antibody-infectious-removed (eSAIR) model under time-varying state-specific control measures. This new toolbox enables the projection of the county-level COVID-19 prevalence over 3109 counties in the continental United States, including -day-ahead risk forecast and the risk related to a travel route. In comparison to the existing temporal risk prediction models, the proposed CA-eSAIR model informs the projected county-level risk to governments and residents of the local coronavirus spread patterns and the associated personal risks at specific geolocations. Such high-resolution risk projection is useful for decision-making on business reopening and resource allocation for COVID-19 tests.
Keywords: cellular automata; coronavirus infectious disease; risk prediction; SAIR model.