Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis
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Abstract
Significant uncertainty has existed about the safety of reopening college and university campuses before the COVID-19 pandemic is better controlled. Moreover, little is known about the effects that on-campus students may have on local higher-risk communities.
Objective
We aimed to estimate the range of potential community and campus COVID-19 exposures, infections, and mortality under various university reopening plans and uncertainties.
Methods
We developed campus-only, community-only, and campus × community epidemic differential equations and agent-based models, with inputs estimated via published and grey literature, expert opinion, and parameter search algorithms. Campus opening plans (spanning fully open, hybrid, and fully virtual approaches) were identified from websites and publications. Additional student and community exposures, infections, and mortality over 16-week semesters were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outliers. Sensitivity analyses were conducted to inform potential effective interventions.
Results
Predicted 16-week campus and additional community exposures, infections, and mortality for the base case with no precautions (or negligible compliance) varied significantly from their medians (4- to 10-fold). Over 5% of on-campus students were infected after a mean of 76 (SD 17) days, with the greatest increase (first inflection point) occurring on average on day 84 (SD 10.2 days) of the semester and with total additional community exposures, infections, and mortality ranging from 1-187, 13-820, and 1-21 per 10,000 residents, respectively. Reopening precautions reduced infections by 24%-26% and mortality by 36%-50% in both populations. Beyond campus and community reproductive numbers, sensitivity analysis indicated no dominant factors that interventions could primarily target to reduce the magnitude and variability in outcomes, suggesting the importance of comprehensive public health measures and surveillance.
Conclusions
Community and campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Public health implications include the need for effective surveillance and flexible campus operations.
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SciScore for 10.1101/2020.08.29.20184366: (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 models were adapted from validated MatLab code used to study other epidemics over several years. MatLabsuggested: (MATLAB, RRID:SCR_001622)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: We detected the following sentences addressing limitations in the study:Like any model-based analysis, results herein have some limitations and simplifications. A common barrier in such models is data availability for input estimation and results …
SciScore for 10.1101/2020.08.29.20184366: (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 models were adapted from validated MatLab code used to study other epidemics over several years. MatLabsuggested: (MATLAB, RRID:SCR_001622)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: We detected the following sentences addressing limitations in the study:Like any model-based analysis, results herein have some limitations and simplifications. A common barrier in such models is data availability for input estimation and results validation (hence our search-based approach). The deterministic ODE modeling framework ignores inherent variability and population heterogeneity,116 motivating our use of Monte Carlo analysis, parameter search repetitions, and varied scenarios. Standard model simplifications include limiting the number of populations (e.g., one overall homogenous community or student population), limiting spread to just SARS-CoV-2 (e.g., ignoring seasonal influenza, the substance abuse co-epidemic, 75,76 and co-spread impacts), and not time-varying precaution compliance as concerns and vigilance relax or heighten over time. Some scenarios also were included for potential insights rather than being feasible in practice (e.g., 75% reduction in R0, near 100% precaution compliance). Results nonetheless offer valuable insights into the range of potential community and student impacts, intervention effectiveness, and inflection criteria at which, if approaching, campuses should close. Further work could expand on these results, including addressing some of the above simplifications made in the interest of time. Work also could occur to determine combined conditions (reduced prevalence, vaccine effectiveness, improved precaution methods, etc.) under which out-comes are both safer and more certain. City- or state-wide decision-m...
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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