Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation

Abstract Many educational institutions have partially or fully closed all operations to cope with the challenges of the ongoing COVID-19 pandemic. In this paper, we explore strategies that such institutions can adopt to conduct safe reopening and resume operations during the pandemic. The research i...

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Autores principales: Ujjal K. Mukherjee, Subhonmesh Bose, Anton Ivanov, Sebastian Souyris, Sridhar Seshadri, Padmavati Sridhar, Ronald Watkins, Yuqian Xu
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Publicado: Nature Portfolio 2021
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spelling oai:doaj.org-article:44f8c9165aac4858abccff614b7ebb1e2021-12-02T16:31:07ZEvaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation10.1038/s41598-021-84192-y2045-2322https://doaj.org/article/44f8c9165aac4858abccff614b7ebb1e2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84192-yhttps://doaj.org/toc/2045-2322Abstract Many educational institutions have partially or fully closed all operations to cope with the challenges of the ongoing COVID-19 pandemic. In this paper, we explore strategies that such institutions can adopt to conduct safe reopening and resume operations during the pandemic. The research is motivated by the University of Illinois at Urbana-Champaign’s (UIUC’s) SHIELD program, which is a set of policies and strategies, including rapid saliva-based COVID-19 screening, for ensuring safety of students, faculty and staff to conduct in-person operations, at least partially. Specifically, we study how rapid bulk testing, contact tracing and preventative measures such as mask wearing, sanitization, and enforcement of social distancing can allow institutions to manage the epidemic spread. This work combines the power of analytical epidemic modeling, data analysis and agent-based simulations to derive policy insights. We develop an analytical model that takes into account the asymptomatic transmission of COVID-19, the effect of isolation via testing (both in bulk and through contact tracing) and the rate of contacts among people within and outside the institution. Next, we use data from the UIUC SHIELD program and 85 other universities to estimate parameters that describe the analytical model. Using the estimated parameters, we finally conduct agent-based simulations with various model parameters to evaluate testing and reopening strategies. The parameter estimates from UIUC and other universities show similar trends. For example, infection rates at various institutions grow rapidly in certain months and this growth correlates positively with infection rates in counties where the universities are located. Infection rates are also shown to be negatively correlated with testing rates at the institutions. Through agent-based simulations, we demonstrate that the key to designing an effective reopening strategy is a combination of rapid bulk testing and effective preventative measures such as mask wearing and social distancing. Multiple other factors help to reduce infection load, such as efficient contact tracing, reduced delay between testing and result revelation, tests with less false negatives and targeted testing of high-risk class among others. This paper contributes to the nascent literature on combating the COVID-19 pandemic and is especially relevant for educational institutions and similarly large organizations. We contribute by providing an analytical model that can be used to estimate key parameters from data, which in turn can be used to simulate the effect of different strategies for reopening. We quantify the relative effect of different strategies such as bulk testing, contact tracing, reduced infectivity and contact rates in the context of educational institutions. Specifically, we show that for the estimated average base infectivity of 0.025 ( $$R_0 = 1.82$$ R 0 = 1.82 ), a daily number of tests to population ratio T/N of 0.2, i.e., once a week testing for all individuals, is a good indicative threshold. However, this test to population ratio is sensitive to external infectivities, internal and external mobilities, delay in getting results after testing, and measures related to mask wearing and sanitization, which affect the base infection rate.Ujjal K. MukherjeeSubhonmesh BoseAnton IvanovSebastian SouyrisSridhar SeshadriPadmavati SridharRonald WatkinsYuqian XuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-24 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Ujjal K. Mukherjee
Subhonmesh Bose
Anton Ivanov
Sebastian Souyris
Sridhar Seshadri
Padmavati Sridhar
Ronald Watkins
Yuqian Xu
Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation
description Abstract Many educational institutions have partially or fully closed all operations to cope with the challenges of the ongoing COVID-19 pandemic. In this paper, we explore strategies that such institutions can adopt to conduct safe reopening and resume operations during the pandemic. The research is motivated by the University of Illinois at Urbana-Champaign’s (UIUC’s) SHIELD program, which is a set of policies and strategies, including rapid saliva-based COVID-19 screening, for ensuring safety of students, faculty and staff to conduct in-person operations, at least partially. Specifically, we study how rapid bulk testing, contact tracing and preventative measures such as mask wearing, sanitization, and enforcement of social distancing can allow institutions to manage the epidemic spread. This work combines the power of analytical epidemic modeling, data analysis and agent-based simulations to derive policy insights. We develop an analytical model that takes into account the asymptomatic transmission of COVID-19, the effect of isolation via testing (both in bulk and through contact tracing) and the rate of contacts among people within and outside the institution. Next, we use data from the UIUC SHIELD program and 85 other universities to estimate parameters that describe the analytical model. Using the estimated parameters, we finally conduct agent-based simulations with various model parameters to evaluate testing and reopening strategies. The parameter estimates from UIUC and other universities show similar trends. For example, infection rates at various institutions grow rapidly in certain months and this growth correlates positively with infection rates in counties where the universities are located. Infection rates are also shown to be negatively correlated with testing rates at the institutions. Through agent-based simulations, we demonstrate that the key to designing an effective reopening strategy is a combination of rapid bulk testing and effective preventative measures such as mask wearing and social distancing. Multiple other factors help to reduce infection load, such as efficient contact tracing, reduced delay between testing and result revelation, tests with less false negatives and targeted testing of high-risk class among others. This paper contributes to the nascent literature on combating the COVID-19 pandemic and is especially relevant for educational institutions and similarly large organizations. We contribute by providing an analytical model that can be used to estimate key parameters from data, which in turn can be used to simulate the effect of different strategies for reopening. We quantify the relative effect of different strategies such as bulk testing, contact tracing, reduced infectivity and contact rates in the context of educational institutions. Specifically, we show that for the estimated average base infectivity of 0.025 ( $$R_0 = 1.82$$ R 0 = 1.82 ), a daily number of tests to population ratio T/N of 0.2, i.e., once a week testing for all individuals, is a good indicative threshold. However, this test to population ratio is sensitive to external infectivities, internal and external mobilities, delay in getting results after testing, and measures related to mask wearing and sanitization, which affect the base infection rate.
format article
author Ujjal K. Mukherjee
Subhonmesh Bose
Anton Ivanov
Sebastian Souyris
Sridhar Seshadri
Padmavati Sridhar
Ronald Watkins
Yuqian Xu
author_facet Ujjal K. Mukherjee
Subhonmesh Bose
Anton Ivanov
Sebastian Souyris
Sridhar Seshadri
Padmavati Sridhar
Ronald Watkins
Yuqian Xu
author_sort Ujjal K. Mukherjee
title Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation
title_short Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation
title_full Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation
title_fullStr Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation
title_full_unstemmed Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation
title_sort evaluation of reopening strategies for educational institutions during covid-19 through agent based simulation
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/44f8c9165aac4858abccff614b7ebb1e
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