Activity Number:
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220
- Frontiers of Spatio-Temporal Statistical Learning in Health Care and Environmental Science
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Type:
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Invited
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Date/Time:
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Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #320504
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Title:
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A Spatiotemporal Epidemiological Prediction Model to Inform County-Level COVID-19 Risk in the United States
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Author(s):
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Yiwang Zhou* and Peter Song
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Companies:
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St. Jude Children's Research Hospital and University of Michigan
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Keywords:
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cellular automata;
coronavirus infectious disease;
risk prediction;
SAIR model
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.