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Adapting a GEOspatial Agent-based model for Covid Transmission (GeoACT) for general use
University of California San Diego

GeoACT (GEOspatial Agent-based model for Covid Transmission) is a designed to simulate a range of intervention scenarios to help schools evaluate their COVID-19 plans to prevent super-spreader events and outbreaks. It consists of several modules, which compute infection risks in classrooms and on school buses, given specific classroom layouts, student population, and school activities. The first version of the model was deployed on the Expanse (and earlier, COMET) resource at SDSC and accessed via the Apache Airavata portal (geoact.org). The second version is a rewrite of the model which makes it easier to adjust to new strains, vaccines and boosters, and include detailed user-defined school schedules, school floor plans, and local community transmission rates. This version is nearing completion. We’ll use Expanse to run additional scenarios using the enhanced model and the newly added meta-analysis module. The current goal is to make the model more general so that it can be used for other health emergencies. GeoACT has been in the news, e.g. UC San Diego Data Science Undergrads Help Keep K-12 Students COVID-Safe, and SDSC Supercomputers Helped Enable Safer School Reopenings  (HPCWire 2022 Editors' Choice Award)

Status: Finishing Up

People with Expertise

Cyd Burrows-Schilling

University of California - San Diego

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Campus Champions

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mentor, CampusChampionsAdmin, research computing facilitator, Affinity Group Leader

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Kenneth Bundy

University of Maine

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mentor

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Wendy Dorman

University of Illinois at Urbana-Champaign

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ACCESS CSSN

Wendy Dorman

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Lauren Hill-Beaton

Rutgers University

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mentor

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David White

Programs

ACCESS CSSN

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cssn, Consultant

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Gabrielle Martinez

Pace University

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student-facilitator

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