COVID-19 Active Surveillance Simulation Case Study - Health and Economic Impacts of Active Surveillance in a School Environment
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Abstract
The COVID-19 pandemic has affected the lives of almost all human beings and has forced stay-at-home mandates across the world. Government and school officials are facing challenging decisions on how to start the new 2020/2021 school year. Almost every school system has chosen a remote learning model for the Fall of 2020 while many are facing financial and logistical challenges.
In this study, we explore the efficacy of an Active Surveillance testing model where a random number of students are tested daily for early detection of asymptomatic patients and for prevention of the infection among the student population. In addition to health impacts, we also analyze the financial impact of deploying the Active Surveillance system in schools while taking into consideration lost workdays of parents, hospitalization costs, and testing costs.
Under the given assumptions, initial modeling results indicate that low Active Surveillance testing rates (between 6-10% daily testing of student population) can help achieve low infection rates (≤10%) among students along with enforcing mitigation procedures, such as wearing masks and social distancing. Without enforcing mitigation procedures, the optimal Active Surveillance rate of 8-10% can also achieve (≤10%) infection rates among student population. The results also demonstrate that Active Surveillance can lower the financial burden of the pandemic by proactively lowering the infection rates among student populations.
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SciScore for 10.1101/2020.10.28.20221416: (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 based our simulation model on the Coronavirus Simulation Matlab program written by Joshua Gafford3, which is a recreation of the Washington Post COVID-19 simulation article listed above. 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: 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 Trial…
SciScore for 10.1101/2020.10.28.20221416: (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 based our simulation model on the Coronavirus Simulation Matlab program written by Joshua Gafford3, which is a recreation of the Washington Post COVID-19 simulation article listed above. 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: 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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