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解读 SARS-CoV-2 血清流行率、死亡和病死率——为改善沟通提出标准化报告的建议。

Interpreting SARS-CoV-2 seroprevalence, deaths, and fatality rate - Making a case for standardized reporting to improve communication.

机构信息

Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA.

Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.

出版信息

Math Biosci. 2021 Mar;333:108545. doi: 10.1016/j.mbs.2021.108545. Epub 2021 Jan 15.

Abstract

The SARS-CoV-2 virus has spread across the world, testing each nation's ability to understand the state of the pandemic in their country and control it. As we looked into the epidemiological data to uncover the impact of the COVID-19 pandemic, we discovered that critical metadata is missing which is meant to give context to epidemiological parameters. In this review, we identify key metadata for the COVID-19 fatality rate after a thorough analysis of mathematical models, serology-informed studies and determinants of causes of death for the COVID-19 pandemic. In doing so, we find reasons to establish a set of standard-based guidelines to record and report the data from epidemiological studies. Additionally, we discuss why standardizing nomenclature is be a necessary component of these guidelines to improve communication and reproducibility. The goal of establishing these guidelines is to facilitate the interpretation of COVID-19 epidemiological findings and data by the general public, health officials, policymakers and fellow researchers. Our suggestions may not address all aspects of this issue; rather, they are meant to be the foundation for which experts can establish and encourage future guidelines throughout the appropriate communities.

摘要

SARS-CoV-2 病毒已在全球范围内传播,考验着每个国家了解本国疫情状况并加以控制的能力。在我们研究流行病学数据以揭示 COVID-19 大流行的影响时,我们发现缺失了关键的元数据,这些数据本应提供流行病学参数的背景信息。在这篇综述中,我们通过对数学模型、基于血清学的研究以及 COVID-19 大流行死亡原因的决定因素进行深入分析,确定了 COVID-19 死亡率的关键元数据。这样做的同时,我们找到了建立一套基于标准的指南的理由,以记录和报告流行病学研究的数据。此外,我们还讨论了为什么标准化命名法是这些指南的必要组成部分,以提高沟通和可重复性。制定这些指南的目的是促进公众、卫生官员、政策制定者和研究人员对 COVID-19 流行病学研究结果和数据的理解。我们的建议可能无法解决这个问题的所有方面;相反,它们旨在为专家提供基础,以便在适当的社区中建立和鼓励未来的指南。

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