Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction
BackgroundU.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders cons...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.856940/full |
| _version_ | 1811170326035300352 |
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| author | Douglas E. Morrison Douglas E. Morrison Douglas E. Morrison Roch Nianogo Roch Nianogo Vladimir Manuel Vladimir Manuel Onyebuchi A. Arah Onyebuchi A. Arah Onyebuchi A. Arah Nathaniel Anderson Tony Kuo Tony Kuo Tony Kuo Moira Inkelas Moira Inkelas |
| author_facet | Douglas E. Morrison Douglas E. Morrison Douglas E. Morrison Roch Nianogo Roch Nianogo Vladimir Manuel Vladimir Manuel Onyebuchi A. Arah Onyebuchi A. Arah Onyebuchi A. Arah Nathaniel Anderson Tony Kuo Tony Kuo Tony Kuo Moira Inkelas Moira Inkelas |
| author_sort | Douglas E. Morrison |
| collection | DOAJ |
| description | BackgroundU.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders consider to be safe.MethodsWe developed an agent-based model of infection dynamics and preventive mitigation designed as a conceptual tool to give school districts basic insights into their options, and to provide optimal flexibility and computational ease as COVID-19 science rapidly evolved early in the pandemic. Elements included distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. Model elements were designed to be updated as the pandemic and scientific knowledge evolve. An online interface enables school districts and their implementation partners to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions.ResultsThe model shows infection dynamics that school districts should consider. For example, under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education.ConclusionsOur model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model was designed in a period of considerable uncertainty and rapidly evolving science. It had practical use early in the pandemic to surface dynamics for school districts and to enable manipulation of parameters as well as rapid update in response to changes in epidemiological conditions and scientific information about COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies. |
| first_indexed | 2024-04-10T16:55:06Z |
| format | Article |
| id | doaj.art-440455cb82d244958d7bf001a4a23747 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-10T16:55:06Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-440455cb82d244958d7bf001a4a237472023-02-07T08:01:20ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-02-011110.3389/fpubh.2023.856940856940Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instructionDouglas E. Morrison0Douglas E. Morrison1Douglas E. Morrison2Roch Nianogo3Roch Nianogo4Vladimir Manuel5Vladimir Manuel6Onyebuchi A. Arah7Onyebuchi A. Arah8Onyebuchi A. Arah9Nathaniel Anderson10Tony Kuo11Tony Kuo12Tony Kuo13Moira Inkelas14Moira Inkelas15Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Public Health Sciences, School of Medicine, University of California, Davis, Davis, CA, United StatesDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United StatesClinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, CA, United StatesClinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United StatesClinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Statistics, College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United StatesClinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United StatesClinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United StatesBackgroundU.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders consider to be safe.MethodsWe developed an agent-based model of infection dynamics and preventive mitigation designed as a conceptual tool to give school districts basic insights into their options, and to provide optimal flexibility and computational ease as COVID-19 science rapidly evolved early in the pandemic. Elements included distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. Model elements were designed to be updated as the pandemic and scientific knowledge evolve. An online interface enables school districts and their implementation partners to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions.ResultsThe model shows infection dynamics that school districts should consider. For example, under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education.ConclusionsOur model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model was designed in a period of considerable uncertainty and rapidly evolving science. It had practical use early in the pandemic to surface dynamics for school districts and to enable manipulation of parameters as well as rapid update in response to changes in epidemiological conditions and scientific information about COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.https://www.frontiersin.org/articles/10.3389/fpubh.2023.856940/fullCOVID-19school districtagent-based modelsecondary transmissionK-6 |
| spellingShingle | Douglas E. Morrison Douglas E. Morrison Douglas E. Morrison Roch Nianogo Roch Nianogo Vladimir Manuel Vladimir Manuel Onyebuchi A. Arah Onyebuchi A. Arah Onyebuchi A. Arah Nathaniel Anderson Tony Kuo Tony Kuo Tony Kuo Moira Inkelas Moira Inkelas Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction Frontiers in Public Health COVID-19 school district agent-based model secondary transmission K-6 |
| title | Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction |
| title_full | Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction |
| title_fullStr | Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction |
| title_full_unstemmed | Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction |
| title_short | Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction |
| title_sort | modeling covid 19 infection dynamics and mitigation strategies for in person k 6 instruction |
| topic | COVID-19 school district agent-based model secondary transmission K-6 |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.856940/full |
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