Modeling infection dynamics and mitigation strategies to support K-6 in-person instruction during the COVID-19 pandemic
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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.
Article activity feed
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SciScore for 10.1101/2021.02.27.21252535: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Notably, while our model includes both symptoms and possible exposure in the daily health screening, current CDC guidance notes limitations of using symptoms in a daily health screening among K-12 students.14 Modeling possible outcomes under different scenarios helps to show how local conditions and program choices affect outcomes that …
SciScore for 10.1101/2021.02.27.21252535: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Notably, while our model includes both symptoms and possible exposure in the daily health screening, current CDC guidance notes limitations of using symptoms in a daily health screening among K-12 students.14 Modeling possible outcomes under different scenarios helps to show how local conditions and program choices affect outcomes that stakeholders care about. As the pandemic progresses, more decision-makers are involved, and each locality has a unique set of political, resource, and behavioral constraints. This model enables localities to see the implications of these constraints and to identify the combination of elements that could work given their constraints. The results flowing from these models can in turn be used to empower action by providing stakeholders with a simple data display and easy interface for parameter estimation, which can in turn inform collective actions may be needed to achieve specific goal targets that they establish for their system. This includes governance, public health, school district administrators, labor unions, parents, and students as well as the broader communities. Models such as this one are useful for considering equity implications in areas with significant regional variation in factors ranging from COVID-19 prevalence to the knowledge and ability to be forthcoming in reporting possible exposures and symptoms. A critical feature of this model is that its parameters can be adapted based on the evolution of COVID- 19 science and the tec...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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