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.
The world has been facing the SARS-CoV-2, a.k.a. COVID-19, pandemic with different preventive methods including social distancing, face masking, screening tests (a.k.a. active surveillance), and vaccination. There are many publications and studies on the efficacy of each of these preventive methods for the last couple of years. Not all methods are readily available in each country and not all methods are accepted by all people in each society. In this study, we explore the interaction of the three preventive methods: face masking, vaccinations, and screening tests. We study a confined space to represent schools, businesses, or healthcare facilities and we model the spread of the COVID-19 virus for a 60-day period among a sample population while varying the percentage of people adopting one or more of the three preventive methods. To interpret the simulation results, we define a (Health Goal) target, for example achieving <5% infection rate, i.e., protecting 95% or more of the sample population. We then construct a (Decision Tree) that depicts all valid combinations that achieve this goal. Multiple scenarios are derived from the decision tree to guide decision makers in drawing effective policies to contain the virus spread. We demonstrate a ramping vaccination rate scenario, a removal of the face-masking mandate scenario, and a cost-minimizing goal scenario. The study highlights the efficacy of combining the three prevention methods to constrain the virus spread among the sample population. For example, results show that a combination of 0% vaccination rate, 6% daily screening test rate, and 80% face masking rate will achieve the target ≥95 protection rate, which can represent a scenario in which vaccination is not yet readily available. As the vaccination rate ramps up to 80% among the sample population, the screening test rate can be 0%, while the face masking rate can be as low as 5% to still achieve the health target. Many other scenarios are derived from this study to meet the defined health target, which represents the flexibility afforded to policy and decision makers when trying to adopt a combination of these preventive methods to contain virus spread. The study also reveals the higher efficiency of either the vaccination or screening test over face masking under the assumed virus transmissibility rates in the study.
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