An Evaluation of Educational Institutions Safe Reopening Strategies for In-Person Classes amid the COVID-19 Pandemic

Ujjal Mukherjee, Subhonmesh Bose, Anton Ivanov, Sebastian Souyris, Sridhar Seshadri, Padmavati Sridhar, Ronald Watkins, Yuqian Xu

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Received date: 12th September 2020

Research Context. Can educational institutions open up safely amid COVID-19? We build an epidemiological model to investigate the strategies necessary for institutions to safely reopen. The four measures that are most relevant for in-person opening are: (i) wide-spread rapid testing, possibly saliva-based, (ii) enforcement of mask-wearing, (iii) social distancing, and (iv) contact tracing. Research Design. Using an analytical setup, we theoretically demonstrate that institutions need to test at a relatively high level (e.g., at least once every week for all individuals) in the initial phases of reopening. Guided by the analytical setup, we derive insights from an agent-based simulation. Contact tracing is relatively more important when the positivity rate from random testing is relatively low, which is likely during the initial phases. An adaptive testing strategy based on positivity rates can help institutions optimally manage the costs and risks of reopening. Finally, to demonstrate the strategies in practice, we provide empirical estimates of some of the educational institutions opening up experience and comment on mitigation strategies. Empirically, we characterize the role of testing using data from the SHIELD program at the University of Illinois at Urbana Champaign (UIUC). Results. We show that increasing the testing levels from 0.2 per capita per day to 0.3 per capita per day can reduce the infectivity from 0.25 to 0.01, with an average slope of the infectivity to the testing curve being 0.35 in this range. We also cross-validate the results with data from a large number of universities in the United States, and show that institutions with higher levels of testing are associated with lower infections.  The estimated marginal effect of increasing testing levels by 1% per capita per day across universities can reduce the positivity by an average of 0.0228% with a 99% confidence interval of [0.0209%-0.0253%]. We also provide an estimate of the locational effects of institutions on mitigation strategies. We estimate from data on 228 different universities across the United States that an increase of infection rate at the county where a university is located by 1% has the potential to increase the institutional infection rate by an average of 0.14% with a 99% confidence interval of [0.032% – 0.248%] across all universities. This indicates that universities are not closed systems, rather they are open systems subject to external influence, and the extent of external influence potential is an important consideration for opening up of universities. Contributions. This paper contributes to the nascent literature on combating the COVID-19 pandemic and is especially relevant for large organizations. This work is motivated and guided by the SHIELD program of UIUC. We provide important policy pointers for the reopening of universities.

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This is an abstract of a preprint hosted on a preprint server, which is currently undergoing peer review at Scientific Reports. The findings have yet to be thoroughly evaluated, nor has a decision on ultimate publication been made. Therefore, the results reported should not be considered conclusive, and these findings should not be used to inform clinical practice, or public health policy, or be promoted as verified information.

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