The COVID-19 Consequences of College Class Continuity Calculator: A Tool to Provide Students and Administrators with Estimated Risks of Returning to Campus
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Abstract
As schools prepare for the start of the Fall 2020 semester, many are struggling to make decisions regarding whether or not to return to on-campus classes or whether to remain fully online. Unfortunately, there is no ‘one-size-fits-all’ answer, and schools must balance their own risks against the costs of remote learning. We present a tool that integrates information about study body composition with predictions of COVID-19 infection rates in order to provide clarity and insight into the decisions facing colleges and universities nationwide. Our tool is freely available and currently hosted at the following location: https://bewicklab.shinyapps.io/covid-1/
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SciScore for 10.1101/2020.07.31.20165761: (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 All code is available at the following Github repository: (https://github.com/bewicklab/COVID19_Consequences_of_College_Class_Continuity_Calculator) Student enrollment data: Student enrollment data was obtained using the custom Python code ‘class_names.py’ to webscrape data on college enrollment from the College Factual website. Pythonsuggested: (IPython, RRID:SCR_001658)‘Current Projection’ assumes mandates are re-imposed for 6 weeks whenever daily deaths rates reach 8 per million. Projection’suggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from Limitat…SciScore for 10.1101/2020.07.31.20165761: (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 All code is available at the following Github repository: (https://github.com/bewicklab/COVID19_Consequences_of_College_Class_Continuity_Calculator) Student enrollment data: Student enrollment data was obtained using the custom Python code ‘class_names.py’ to webscrape data on college enrollment from the College Factual website. Pythonsuggested: (IPython, RRID:SCR_001658)‘Current Projection’ assumes mandates are re-imposed for 6 weeks whenever daily deaths rates reach 8 per million. Projection’suggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Although our tool is useful for helping to guide decision-making regarding f2f classes in Fall 2020, there are a few caveats. First, our models are based on student enrollment from the 2017-2018 year, since this is the most recently available dataset. Any school that has seen a significant change (increase, decrease, or change in composition across states) in enrollment over the last two years may find that predictions are less accurate. In addition, our tool is based on IHME predictions. Thus, the accuracy of our models hinge on the accuracy of the IHME predictions. Preliminary results suggest relative agreement between our predictions and current testing results. For example, testing at West Virginia University (https://presidentgee.wvu.edu/messages/phased-return-to-morgantown-campus-july-27) suggests a ∼0.2% positive rate, which is commensurate with our estimated 0.17% positive rate. For this estimate we assumed a start date 08/30/2020, counted only active infections, and assumed an infectious period of 10 days, an incubation period of 5 days, 100% testing and a false negative rate of 0%. Beyond the accuracy of the underlying models, our tool relies on a few additional assumptions. First, our tool allows users to pick the infectious period and incubation period. Although we preset these parameters at values consistent with CDC recommendations10 and existing research studies11, users are free to alter both, and this could impact predictions. We allow user input on these par...
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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