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Activity Number: 220 - Frontiers of Spatio-Temporal Statistical Learning in Health Care and Environmental Science
Type: Invited
Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320504
Title: A Spatiotemporal Epidemiological Prediction Model to Inform County-Level COVID-19 Risk in the United States
Author(s): Yiwang Zhou* and Peter Song
Companies: St. Jude Children's Research Hospital and University of Michigan
Keywords: cellular automata; coronavirus infectious disease; risk prediction; SAIR model
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 t-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.


Authors who are presenting talks have a * after their name.

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