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This content will become publicly available on July 6, 2022

Title: Informing University COVID-19 Decisions Using Simple Compartmental Models
Tracking the COVID-19 pandemic has been a major challenge for policy makers. Although, several efforts are ongoing for accurate forecasting of cases, deaths, and hospitalization at various resolutions, few have been attempted for college campuses despite their potential to become COVID-19 hot-spots. In this paper, we present a real-time effort towards weekly forecasting of campus-level cases during the fall semester for four universities in Virginia, United States. We discuss the challenges related to data curation. A causal model is employed for forecasting with one free time-varying parameter, calibrated against case data. The model is then run forward in time to obtain multiple forecasts. We retrospectively evaluate the performance and, while forecast quality suffers during the campus reopening phase, the model makes reasonable forecasts as the fall semester progresses. We provide sensitivity analysis for the several model parameters. In addition, the forecasts are provided weekly to various state and local agencies.
Authors:
; ; ;
Award ID(s):
1633028 1916805 1918656 2028004 2027541
Publication Date:
NSF-PAR ID:
10313656
Journal Name:
ArXivorg
ISSN:
2331-8422
Sponsoring Org:
National Science Foundation
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