Simulation-based what-if analysis for controlling the spread of Covid-19 in universities.
A simulation model is developed to analyze the spread of covid-19 in universities. The model can be used to conduct a what-if analysis and estimate infection cases under different policies. For proof-of-concept, the model is simulated for a hypothetical university of 25,000 students and 3,000 faculty/staff in a U.S. college town. Simulation results show that early outbreaks are very likely, and there is no silver bullet to avoid them. Instead, a combination of policies should be carefully implemented. The results suggest (almost) full remote university operations from the beginning of the semester. In a less-preferred alternative, if universities decide to have students attend in person, they should encourage remote operations for high-risk individuals, conduct frequent rapid tests, enforce mask use, communicate with students and employees about the risks, and promote social distancing. Universities should be willing to move to remote operations if cases rise. Under this scenario, and considering implementation challenges, many universities are still likely to experience an early outbreak, and the likelihood of having a case of death is worrisome. In the long run, students and faculty react to the risks, and even if universities decide to continue operations, classes are likely to have very low in-person attendance. Overall, our analysis depicts several sources of system complexities, negative unintended consequences of relying on a single policy, non-linear incremental effects, and positive synergies of implementing multiple policies. A simulation platform for a what-if analysis is offered so marginal effectiveness of different policies and different decision-making thresholds for closure can be tested for universities of varying populations..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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| Enthalten in: |
PLoS ONE - 16(2021), 2, p e0246323 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Navid Ghaffarzadegan [VerfasserIn] |
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| Links: |
doi.org [kostenfrei] |
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| Themen: |
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| doi: |
10.1371/journal.pone.0246323 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ004683781 |
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| 520 | |a A simulation model is developed to analyze the spread of covid-19 in universities. The model can be used to conduct a what-if analysis and estimate infection cases under different policies. For proof-of-concept, the model is simulated for a hypothetical university of 25,000 students and 3,000 faculty/staff in a U.S. college town. Simulation results show that early outbreaks are very likely, and there is no silver bullet to avoid them. Instead, a combination of policies should be carefully implemented. The results suggest (almost) full remote university operations from the beginning of the semester. In a less-preferred alternative, if universities decide to have students attend in person, they should encourage remote operations for high-risk individuals, conduct frequent rapid tests, enforce mask use, communicate with students and employees about the risks, and promote social distancing. Universities should be willing to move to remote operations if cases rise. Under this scenario, and considering implementation challenges, many universities are still likely to experience an early outbreak, and the likelihood of having a case of death is worrisome. In the long run, students and faculty react to the risks, and even if universities decide to continue operations, classes are likely to have very low in-person attendance. Overall, our analysis depicts several sources of system complexities, negative unintended consequences of relying on a single policy, non-linear incremental effects, and positive synergies of implementing multiple policies. A simulation platform for a what-if analysis is offered so marginal effectiveness of different policies and different decision-making thresholds for closure can be tested for universities of varying populations. | ||
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