建模 COVID-19 感染动态及面对面 K-6 教学的缓解策略。

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

机构信息

Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States.

Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States.

出版信息

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

DOI:10.3389/fpubh.2023.856940

PMID:36825137

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9941563/

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.

摘要

背景

由于 2019 年冠状病毒病（COVID-19）大流行，美国学校关闭，导致远程学习时间延长，并对家庭造成社会和经济影响。由于对病毒动态的不确定性，学区难以制定所有利益相关者都认为安全的缓解计划。

方法

我们开发了一种基于代理的感染动态和预防缓解模型，旨在作为一种概念工具，使学区对其选择有基本的了解，并在 COVID-19 科学在大流行早期迅速发展时提供最佳的灵活性和计算简便性。元素包括距离、健康行为、监测和症状检测、日常症状和暴露筛查、检疫政策和疫苗接种。模型元素旨在随着大流行和科学知识的发展而更新。在线界面使学区及其实施合作伙伴能够根据各种合理的流行病学和政策假设，探索干预措施对州和地方关注的结果的影响。

结果

该模型显示了学区应考虑的感染动态。例如，在默认假设下，二级感染率和学校出勤率受监测检测方案、疫苗接种率、班级规模以及安全教育的有效性的影响很大。

结论

我们的模型帮助决策者考虑缓解方案和学校感染风险动态如何影响他们关注的结果。该模型是在相当不确定和科学迅速发展的时期设计的。它在大流行早期具有实际用途，可以为学区揭示动态，并能够操纵参数，以及快速更新，以应对流行病学条件的变化以及有关 COVID-19 传播动态、检测和疫苗接种资源以及缓解策略可靠性的科学信息。