High resolution proximity statistics as early warning for US universities reopening during COVID-19
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Abstract
Reopening of colleges and universities for the Fall semester of 2020 across the United States has caused significant 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.
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SciScore for 10.1101/2020.11.21.20236042: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We test Granger causality using python statsmodels [15] and report value for SSR-Chi square test. pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not …
SciScore for 10.1101/2020.11.21.20236042: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We test Granger causality using python statsmodels [15] and report value for SSR-Chi square test. pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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