Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction

Douglas E Morrison; Roch Nianogo; Vladimir Manuel; Onyebuchi A Arah; Nathaniel Anderson; Tony Kuo; Moira Inkelas

doi:10.3389/fpubh.2023.856940

Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction

Front Public Health. 2023 Feb 7:11:856940. doi: 10.3389/fpubh.2023.856940. eCollection 2023.

Authors

Douglas E Morrison^{1

2

3}, Roch Nianogo^{1

4}, Vladimir Manuel^{4

5}, Onyebuchi A Arah^{1

4

6}, Nathaniel Anderson⁷, Tony Kuo^{1

4

5}, Moira Inkelas^{4

7}

Affiliations

¹ Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States.
² Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States.
³ Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, CA, United States.
⁴ Clinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, CA, United States.
⁵ Department of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
⁶ Department of Statistics, College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, United States.
⁷ Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States.

Abstract

Background: U.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.

Methods: We 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.

Results: The 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.

Conclusions: Our 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.

Keywords: COVID-19; K-6; agent-based model; school district; secondary transmission.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

COVID-19* / epidemiology
COVID-19* / prevention & control
Humans
Quarantine
Reproducibility of Results
SARS-CoV-2
Schools

Abstract

Publication types

MeSH terms

Grants and funding