Modeling infection dynamics and mitigation strategies to support K-6 in-person instruction during the COVID-19 pandemic

Abstract

Objective: To support safer in-person K-6 instruction during the coronavirus disease 2019 (COVID- 19) pandemic by providing public health authorities and school districts with a practical model of transmission dynamics and mitigation strategies.

Methods: We developed an agent-based model of infection dynamics and preventive mitigation strategies such as distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. The model parameters can be updated as the science evolves and are adjustable via an online user interface, enabling users to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions.

Results: 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's parameters can be immediately updated in response to changes in epidemiological conditions, science of COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.