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美国情景建模中心对公共卫生的影响。

Public health impact of the U.S. Scenario Modeling Hub.

机构信息

CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA.

CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA.

出版信息

Epidemics. 2023 Sep;44:100705. doi: 10.1016/j.epidem.2023.100705. Epub 2023 Jul 18.

DOI:10.1016/j.epidem.2023.100705
PMID:37579585
Abstract

Beginning in December 2020, the COVID-19 Scenario Modeling Hub has provided quantitative scenario-based projections for cases, hospitalizations, and deaths, aggregated across up to nine modeling groups. Projections spanned multiple months into the future and provided timely information on potential impacts of epidemiological uncertainties and interventions. Projections results were shared with the public, public health partners, and the Centers for Disease Control COVID-19 Response Team. The projections provided insights on situational awareness and informed decision-making to mitigate COVID-19 disease burden (e.g., vaccination strategies). By aggregating projections from multiple modeling teams, the Scenario Modeling Hub provided rapidly synthesized information in times of great uncertainty and conveyed possible trajectories in the presence of emerging variants. Here we detail several use cases of these projections in public health practice and communication, including assessments of whether modeling results directly or indirectly informed public health communication or guidance. These include multiple examples where comparisons of projected COVID-19 disease outcomes under different vaccination scenarios were used to inform Advisory Committee for Immunization Practices recommendations. We also describe challenges and lessons learned during this highly beneficial collaboration.

摘要

自 2020 年 12 月以来,COVID-19 情景建模中心一直提供基于定量情景的病例、住院和死亡预测,汇总了多达 9 个建模小组的数据。预测涵盖未来多个月,并及时提供了有关流行病学不确定性和干预措施潜在影响的信息。预测结果与公众、公共卫生合作伙伴和疾病控制与预防中心 COVID-19 应对小组共享。这些预测结果提供了对情况的了解,并为减轻 COVID-19 疾病负担(例如疫苗接种策略)做出决策提供了信息。通过汇总来自多个建模团队的预测结果,情景建模中心在高度不确定的时期提供了快速综合信息,并在出现新变体时传达了可能的轨迹。在这里,我们详细介绍了这些预测在公共卫生实践和传播中的几个用例,包括评估建模结果是否直接或间接为公共卫生沟通或指导提供信息。其中包括多个例子,比较了不同疫苗接种情景下 COVID-19 疾病结果的预测,以告知免疫实践咨询委员会的建议。我们还描述了在这一非常有益的合作过程中遇到的挑战和经验教训。

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