Entry screening and multi-layer mitigation of COVID-19 cases for a safe university reopening

Elbanna, Ahmed; Wong, George N.; Weiner, Zach J.; Wang, Tong; Zhang, Hantao; Liu, Zhiru; Tkachenko, Alexei V.; Maslov, Sergei; Goldenfeld, Nigel

doi:10.1101/2020.08.29.20184473

Cited by 19 publications

(22 citation statements)

References 24 publications

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“…Those strategies were combined with regular testing to rapidly detect and quarantine infected students. Previous modeling studies have investigated different aspects of COVID-19 spread on college campuses [2][3][4][5][6][7]. For example, Paltiel et al [8] used a simple homogeneous mixing SEIR compartmental model to evaluate the effectiveness of screening strategies in a college with 5000 students; Weeden and Gornwell [9] designed student contact networks though transcript data and used these networks to studied the effectiveness of small course enrollments on reducing the risk of epidemic spread.…”

Section: Introductionmentioning

confidence: 99%

Individual-based modeling of COVID-19 transmission in college communities

Huang

Ndeffo-Mbah

Mondal

et al. 2021

Preprint

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The ongoing COVID-19 pandemic has created major public health and socio-economic challenges across the United States. Among them are challenges to the educational system where college administrators are struggling with the questions of how to reopen in-person activities while prioritizing student safety. To help address this challenge, we developed a flexible computational framework to model the spread and control of COVID-19 on a residential college campus. The modeling framework accounts for heterogeneity in social interactions, activities, disease progression, and control interventions. The relative contribution of classroom, dorm, and social activities to disease transmission were explored. We observed that the dorm has the highest contribution to disease transmission followed by classroom and social activities. Without vaccination, frequent (weekly) random testing coupled with risk reduction measures (e.g. facial mask,) in classroom, dorm, and social activities is the most effective control strategy to mitigate the spread of COVID-19 on college campuses. Moreover, since random screening testing allows for the successful and early detection of both asymptomatic and symptomatic individuals, it successfully reduces the transmission rate such that the maximum quarantine capacity is far lower than expected to further reduce the economic burden caused from quarantine. With vaccination, herd immunity is estimated to be achievable by 50% to 80% immunity coverage. In the absence of herd immunity, simulations indicate that it is optimal to keep some level of transmission risk reduction measures in classroom, dorm, and social activities, while testing at a lower frequency. Though our quantitative results are likely provisional on our model assumptions, extensive sensitivity analysis confirms the robustness of their qualitative nature.

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Section: Introductionmentioning

confidence: 99%

Individual-based modeling of COVID-19 transmission in college communities

Huang

Ndeffo-Mbah

Mondal

et al. 2021

Preprint

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“…For universities and colleges, test allocation needs to be customized according to the local disease rates and community testing capacity [18], as are often characterized by the budget for testing, the total number of tests available, the observed number of positive tests and test positive rate ( Table S4 ). As tests of different costs and accuracy become available, thequestion of test allocation becomes even more challenging.…”

Section: Resultsmentioning

confidence: 99%

“…Extension to allocating two tests. For universities and colleges, test allocation needs to be customized according to the local disease rates and community testing capacity [18], as are often characterized by the budget for testing, the total number of tests available, the observed number of positive tests and test positive rate (Table S4) [19,20,22]. Mirroring current market information, we set the price for the rapid antigen test and for the RT-PCR test as $5 and $135, respectively, and we assume test sensitivity values are 0.45 and 0.9, respectively [22,21].…”

Section: Figure 2 a And B Corresponds To The Setting With Limited Numbementioning

confidence: 99%

Optimal test allocation strategy during the COVID-19 pandemic and beyond

Beesley

Lee

et al. 2020

Preprint

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Testing for active SARS-CoV-2 infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies in the presence of limited resources. Inthis paper, we provide a mathematical framework for defining anoptimal strategy for allocating viral tests. The framework accounts for imperfect test results, selective testing in certain high-risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infected cases, but can also be used for long-time surveillance to monitor for new outbreaks, which will be especially important during ongoing vaccine distribution across the world. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID-19 outbreak in New York City. We extend our proposed method to address the challenge of allocating two different types of tests with different costs and accuracy (for example, the expensive but more accurate RT-PCR test versus the cheap but less accurate rapid antigen test), administered under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision-making at a community level and adapting to different stages of an epidemic, and it has broader relevance beyond the COVID-19 outbreak.

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“…Previous studies modeling university populations did not account for infections in the broader off-campus community. 20-24 However, we have shown that including the general population when modeling COVID-19 transmission on university campuses is critical, since this population bears the brunt of the incremental morbidity and mortality burden of COVID-19. As a result, university policies that either discourage student return to the community, 54 such as offering coursework fully online, or mitigate COVID-19 risks for students returning to campus, such as screening for COVID-19 symptoms and routine COVID-19 testing in asymptomatic individuals, can have substantial impacts on the city’s COVID-19 burden.…”

Section: Discussionmentioning

confidence: 99%

“…Given the unique features of university communities, several studies have modeled COVID-19 transmission dynamics specifically on university campuses and evaluated potential mitigation strategies. 18,20-24 These studies used mathematical models of viral transmission dynamics, tailored to reflect a university context, in order to evaluate testing and contact tracing strategies, largely focusing on the question of how frequently to test asymptomatic students. All analyses concluded that frequent testing (sometimes multiple times a week) would be needed to contain COVID-19 outbreaks on campus.…”

Section: Introductionmentioning

confidence: 99%

Impact of University Re-Opening on Total Community Covid-19 Burden

Cipriano

Haddara

Zaric

et al. 2020

Preprint

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Purpose: Post-secondary students have higher than average contacts than the general population due to congregate living, use of public transit, high-density academic and social activities, and employment in the services sector. We evaluated the impact of a large student population returning to a mid-sized city currently experiencing a low rate of COVID-19 on community health outcomes. We consider whether targeted routine or one-time screening in this population can mitigate community COVID-19 impacts. Methods: We developed a dynamic transmission model of COVID-19 subdivided into three interacting populations: general population, university students, and long-term care residents. We parameterized the model using the medical literature and expert opinion. We calibrated the model to the observed outcomes in a mid-sized Canadian city between March 1 and August 15, 2020 prior to the arrival of a relatively large post-secondary student population. We evaluated the impact of the student population (20,000 people arriving on September 1) on cumulative COVID-19 infections over the fall semester, the timing of peak infections, the timing and peak level of critical care occupancy, and the timing of re-engaged social and economic restrictions. We consider multiple scenarios with different student and general population COVID-19 prevention behaviours as well as different COVID-19 screening strategies in students. Results: In a city with low levels of COVID-19 activity, the return of a relatively large student population substantially increases the total number of COVID-19 infections in the community. In a scenario in which students immediately engage in a 24% contact reduction compared to pre-COVID levels, the total number of infections in the community increases by 87% (from 3,900 without the students to 7,299 infections with the students), with 71% of the incremental infections occurring in the general population, causing social and economic restrictions to be re-engaged 3 weeks earlier and an incremental 17 COVID-19 deaths. Scenarios in which students have an initial, short-term increase in contacts with other students before engaging in contact reduction behaviours can increase infections in the community by 150% or more. In such scenarios, screening asymptomatic students every 5 days reduces the number of infections attributable to the introduction of the university student population by 42% and delays the re-engagement of social and economic restrictions by 1 week. Compared to screening every 5 days, one-time mass screening of students prevents fewer infections, but is highly efficient in terms of infections prevented per screening test performed. Discussion: University students are highly inter-connected with the city communities in which they live and go to school, and they have a higher number of contacts than the general population. High density living environments, enthusiasm for the new school year, and relatively high rates of asymptomatic presentation may decrease their self-protective behaviours and contribute to increased community transmission of COVID-19 affecting at-risk members of the city community. Screening targeted at this population provides significant public health benefits to the community through averted infections, critical care admissions, and COVID-19 deaths.

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Entry screening and multi-layer mitigation of COVID-19 cases for a safe university reopening

Cited by 19 publications

References 24 publications

Individual-based modeling of COVID-19 transmission in college communities

Individual-based modeling of COVID-19 transmission in college communities

Optimal test allocation strategy during the COVID-19 pandemic and beyond

Impact of University Re-Opening on Total Community Covid-19 Burden

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