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Title: High resolution proximity statistics as early warning for US universities reopening during COVID-19
Reopening of colleges and universities for the Fall semester of 2020 across the United States has caused signi ficant COVID-19 case spikes, requiring reactive responses such as temporary closures and switching to online learning. Until sufficient levels of immunity are reached through vaccination, Institutions of Higher Education will need to balance academic operations with COVID-19 spread risk within and outside the student community. In this work, we study the impact of proximity statistics obtained from high resolution mobility traces in predicting case rate surges in university counties. We focus on 50 land-grant university counties (LGUCs) across the country and show high correlation (PCC > 0.6) between proximity statistics and COVID-19 case rates for several LGUCs during the period around Fall 2020 reopenings. These observations provide a lead time of up to 3 weeks in preparing resources and planning containment efforts. We also show how features such as total population, population affiliated with university, median income and case rate intensity could explain some of the observed high correlation. We believe these easily explainable mobility metrics along with other disease surveillance indicators can help universities be better prepared for the Spring 2021 semester.
Authors:
; ; ; ; ; ;
Award ID(s):
1633028 1443054 1916805 1918656 2028004 2027541
Publication Date:
NSF-PAR ID:
10213772
Journal Name:
medRxiv
Sponsoring Org:
National Science Foundation
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