Evaluating trade-offs between COVID-19 prevention and learning loss: an agent-based simulation analysis
The COVID-19 pandemic presented significant challenges in educational settings. Schools implemented a variety of COVID-19 mitigation strategies, some of which were controversial due to potential disruptions to in-person learning. We developed an agent-based model of COVID-19 in a US high school sett...
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The Royal Society
2025-04-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.231842 |
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| author | Kenneth Chen Eva A. Enns |
| author_facet | Kenneth Chen Eva A. Enns |
| author_sort | Kenneth Chen |
| collection | DOAJ |
| description | The COVID-19 pandemic presented significant challenges in educational settings. Schools implemented a variety of COVID-19 mitigation strategies, some of which were controversial due to potential disruptions to in-person learning. We developed an agent-based model of COVID-19 in a US high school setting to evaluate potential trade-offs between preventing COVID-19 infections versus avoiding in-person learning loss under different mitigation policies in a post-Omicron context. Mitigation policies included isolation alone and in combination with quarantine of exposed students, weekly testing of all students or testing of exposed students (‘test-to-stay’) under different scenarios of mask use and booster dose uptake. Outcomes were simulated over an 11 week trimester. We found that requiring a full 5 or 10 day quarantine of exposed students reduced COVID-19 infections by five to sevenfold relative to isolation alone, but at a cost of nearly 40% learning days lost. Test-to-stay achieved nearly the same level of infection reduction with lower levels of learning loss. Weekly testing also reduced COVID-19 infections but was less effective and incurred higher learning loss than test-to-stay. Universal masking and increased vaccination not only reduced infections at no cost to learning but also synergized with other strategies to reduce trade-offs. |
| format | Article |
| id | doaj-art-bbe1ae385fd2419bb8e42f3dc62a6ffc |
| institution | DOAJ |
| issn | 2054-5703 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-bbe1ae385fd2419bb8e42f3dc62a6ffc2025-08-20T03:13:23ZengThe Royal SocietyRoyal Society Open Science2054-57032025-04-0112410.1098/rsos.231842Evaluating trade-offs between COVID-19 prevention and learning loss: an agent-based simulation analysisKenneth Chen0Eva A. Enns1Harvard University, Cambridge, MA, USADivision of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, USAThe COVID-19 pandemic presented significant challenges in educational settings. Schools implemented a variety of COVID-19 mitigation strategies, some of which were controversial due to potential disruptions to in-person learning. We developed an agent-based model of COVID-19 in a US high school setting to evaluate potential trade-offs between preventing COVID-19 infections versus avoiding in-person learning loss under different mitigation policies in a post-Omicron context. Mitigation policies included isolation alone and in combination with quarantine of exposed students, weekly testing of all students or testing of exposed students (‘test-to-stay’) under different scenarios of mask use and booster dose uptake. Outcomes were simulated over an 11 week trimester. We found that requiring a full 5 or 10 day quarantine of exposed students reduced COVID-19 infections by five to sevenfold relative to isolation alone, but at a cost of nearly 40% learning days lost. Test-to-stay achieved nearly the same level of infection reduction with lower levels of learning loss. Weekly testing also reduced COVID-19 infections but was less effective and incurred higher learning loss than test-to-stay. Universal masking and increased vaccination not only reduced infections at no cost to learning but also synergized with other strategies to reduce trade-offs.https://royalsocietypublishing.org/doi/10.1098/rsos.231842COVID-19mathematical modellinglearning loss |
| spellingShingle | Kenneth Chen Eva A. Enns Evaluating trade-offs between COVID-19 prevention and learning loss: an agent-based simulation analysis Royal Society Open Science COVID-19 mathematical modelling learning loss |
| title | Evaluating trade-offs between COVID-19 prevention and learning loss: an agent-based simulation analysis |
| title_full | Evaluating trade-offs between COVID-19 prevention and learning loss: an agent-based simulation analysis |
| title_fullStr | Evaluating trade-offs between COVID-19 prevention and learning loss: an agent-based simulation analysis |
| title_full_unstemmed | Evaluating trade-offs between COVID-19 prevention and learning loss: an agent-based simulation analysis |
| title_short | Evaluating trade-offs between COVID-19 prevention and learning loss: an agent-based simulation analysis |
| title_sort | evaluating trade offs between covid 19 prevention and learning loss an agent based simulation analysis |
| topic | COVID-19 mathematical modelling learning loss |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.231842 |
| work_keys_str_mv | AT kennethchen evaluatingtradeoffsbetweencovid19preventionandlearninglossanagentbasedsimulationanalysis AT evaaenns evaluatingtradeoffsbetweencovid19preventionandlearninglossanagentbasedsimulationanalysis |