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Review 1: "College campuses and COVID-19 mitigation: clinical and economic value"

This is a comprehensive model that covers a timely topic; however, the many estimations that went into the model, as well as the use of "contact-hours" as a key parameter, may make the conclusions subject to uncertainty.

Published onSep 30, 2020
Review 1: "College campuses and COVID-19 mitigation: clinical and economic value"
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key-enterThis Pub is a Review of
College campuses and COVID-19 mitigation: clinical and economic value
Description

Background: Decisions around US college and university operations will affect millions of students and faculty amidst the COVID-19 pandemic. We examined the clinical and economic value of different COVID-19 mitigation strategies on college campuses. Methods: We used the Clinical and Economic Analysis of COVID-19 interventions (CEACOV) model, a dynamic microsimulation that tracks infections accrued by students and faculty, accounting for community transmissions. Outcomes include infections, $/infection-prevented, and $/quality-adjusted-life-year ($/QALY). Strategies included extensive social distancing (ESD), masks, and routine laboratory tests (RLT). We report results per 5,000 students (1,000 faculty) over one semester (105 days). Results: Mitigation strategies reduced COVID-19 cases among students (faculty) from 3,746 (164) with no mitigation to 493 (28) with ESD and masks, and further to 151 (25) adding RLTq3 among asymptomatic students and faculty. ESD with masks cost $168/infection-prevented ($49,200/QALY) compared to masks alone. Adding RLTq3 ($10/test) cost $8,300/infection-prevented ($2,804,600/QALY). If tests cost $1, RLTq3 led to a favorable cost of $275/infection-prevented ($52,200/QALY). No strategies without masks were cost-effective. Conclusion: Extensive social distancing with mandatory mask-wearing could prevent 87% of COVID-19 cases on college campuses and be very cost-effective. Routine laboratory testing would prevent 96% of infections and require low cost tests to be economically attractive.

RR:C19 Evidence Scale rating by reviewer:

  • Potentially informative. The main claims made are not strongly justified by the methods and data, but may yield some insight. The results and conclusions of the study may resemble those from the hypothetical ideal study, but there is substantial room for doubt. Decision-makers should consider this evidence only with a thorough understanding of its weaknesses, alongside other evidence and theory. Decision-makers should not consider this actionable, unless the weaknesses are clearly understood and there is other theory and evidence to further support it.

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Review:

The authors presented a cost-effectiveness analysis built on a transmission model to compare different COVID-19 mitigation strategies on college campuses in the US. While I do think this research question is very important, especially many universities in the US are reopening in the fall 2020 semester amid the COVID-19 pandemic, the underlying assumptions of the transmission model need further validations and therefore the findings are subject to potential uncertainties.

Please see below for my main concerns, mainly about the transmission model:

1. The authors mentioned that CEACOV model was used to simulate the SARS-CoV-2 transmission in the campus. However, it was not clear whether the model assumed a close population of students, faculty and community members. How was the introduction of infections from the community (outside the cohort of 105,000 individuals) modelled? Was entry screening applied in the campus?

2. Since the force of infection of the introduction is likely to be proportional to the prevalence of the nearby communities, universities in different states in the US are under different risks of introduction. It seems to me inappropriate to assume only one transmission scenario (with parameters in Table 1) and perform the subsequent cost-effectiveness analysis.

3. The transmission of SARS-CoV-2 was simulated with the Table 1 parameters in the CEACOV model. Unlike the commonly used approach in infectious disease modeling, the simulation relies on 1) contact hours within and across transmission groups, 2) the infectivity per contact hour from a Wuhan household study, and 3) reduction of contact hours from NPI implementations. However, the estimates from the Wuhan study might not be appropriate to use because it’s from a household study during the lock-down. Moreover, there is not enough evidence that the probability of transmission is linearly proportional to the length of contact hours yet.

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