Can educational institutions open up safely amid COVID-19? We build an epidemiological model to investigate the strategies necessary for institutions to 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. We demonstrate that institutions need to test at a relatively high level (e.g., at least once every week) in the initial phases of reopening. Contact tracing is relatively more important when the positivity rate from random testing is relatively low, which is likely during the initial phases. A Bayesian adaptive testing strategy based on positivity rates can help institutions optimally manage the costs and risks of reopening. This paper contributes to the nascent literature on combating the COVID-19 pandemic and is especially relevant for large-scale organizations. This work is motivated and guided by the SHIELD program of UIUC.
Reopening Strategies Amid COVID-19 strategies.
For example, only testing without proper mask enforcement and social distancing will require testing almost every individual every day for safe reopening. We find optimally allocating testing capacity between random testing and contact tracing is important. Interestingly, and somewhat counterintuitively, the value of contact tracing is higher when the positivity rate from random testing is relatively lower. Positivity rates from random testing is an indicator of current and future infections. At low positivity rates, the detection rates from random testing is low. Therefore, in the initial stages of reopening, when the infection load is likely to be lower, focusing greater efforts toward contact tracing is important. However, contact tracing needs to be optimally combined with random testing. We demonstrate that given a probability of infection transmission of 5%, and contact rate of 10 individuals per day, a somewhat typical scenario, every individual needs to be tested once every 5 days or more for dampening infections in large educational institutions. Rather than adopting a fixed testing capacity, a flexible adaptive system based on Bayesian updating of estimated positivity rates of testing can be more cost efficient. During the initial stages of reopening, it is important to test at a high level, and the testing levels can be reduced adaptively as the infection load (positivity rate) reduces. The adaptive testing strategy can be seen as a risk-sensitive strategy, since it accounts for the latent risk of COVID-19 transmission.
CONCLUSIONS
The reopening of institutions during the COVID-19 pandemic is challenging. Given reasonable levels of mask enforcement (5% chance of infection transmission given contact) and social distancing (5 contacts per person per day), for large institutions such as universities and colleges, a testing level of 𝑇 𝑁 ≥ 0.3 can be sufficient to dampen the spread of the disease. This translates into testing every individual twice a week. However, if this level of testing is not possible, then the shortfall can be compensated by ensuring higher stringency in mask enforcement and social distancing. If the testing level is around 𝑇 𝑁 ≥ 0.1, then the average contact rates need to drop to 1 contact per day. These results are subject to the mathematical abstractions of simulation; however, these results provide a directional understanding of the combination of strategies that are important to consider while reopening institutions. We summarize the findings and the suggested strategy in Figure 3. Figure 3 provides a heat-map for safe reopening strategies and demonstrates the interaction of mask wearing, which determines the infectivity upon contact with infected individuals, social distancing, which determines the contact rate of individuals, and the testing per person per day or the test capacity to population ratio under adaptive testing, subject to maximum capacity as shown. The metric of performance is the area under the susceptible curve, which is a function of the number of persons not infected at any point of time. The area under the susceptible curve is determined by the average basic reproduction number of an epidemic. While the basic reproduction number is an instantaneous measure, the area under the susceptible curve is a cumulative measure. From Figure 3, we observe that an institution needs to adapt to the estimates of infectivity and contact rates, and adapt to changes in infectivity and contact rates. We have included several scenarios that provide a fairly comprehensive estimate of the rate of testing required. Many organizations are testing at a significant high level; for instance, the University of Illinois at Urbana-Champaign has been testing at a rate of 10,000 ( 𝑇 𝑁 = 0.2) individuals every day for a population of approximately 50,000 individuals on campus under the SHIELD program, using a saliva-based rapid testing methodology. Some of the initial reopening experience confirms the value of a combination of strategies. Indiana University suspended all in-person activities in certain student housings after a rapid rise in COVID-19 cases after reopening21 . Per a recent media report22 , several universities have more than 500 cases, such as the University of Alabama at Birmingham (972 cases), University of North Carolina at Chapel Hill (835 cases), University of Central Florida (727 cases), Auburn University in Alabama (557 cases), Texas A&M University (500 cases), University of Notre Dame (473 cases), and University of Illinois at Urbana-Champaign (448 cases), within days and weeks of reopening. Another study23 indicated that colleges and universities would need to test every student once every two days to reopen safely. These outcomes and studies support the insights from our paper.
In closing, we submit that the investigation into reopening strategies is subject to some limitations. The simulations demonstrated here are hypothetical and do not represent the practical complexities of a real organization. Furthermore, actual implementation will entail additional organizational, social, or political constraints that have not been considered in the paper. Nevertheless, we believe that the results presented here provide significant practical insights, at least in a conceptual and directional sense, that can effectively enable institutions to reopen while controlling the risk of COVID-19 spread within the organization. Finally, we believe that different universities and institutions would need to customize the right combination of strategies based on the realities of reopening and the practicality of social distancing and other preventative measure adoption. Therefore, one size does not fit all, and adaptive customization of strategies is essential for the safe reopening of institutions
Reference & Source information: http://connect.medrxiv.org/
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