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COVID-19 期间研究的可重复性:从空间分析角度审视人口密度与基本繁殖数的案例

Reproducibility of Research During COVID-19: Examining the Case of Population Density and the Basic Reproductive Rate from the Perspective of Spatial Analysis.

作者信息

Paez Antonio

机构信息

School of Earth Environment and Society McMaster University Hamilton Canada.

出版信息

Geogr Anal. 2021 Nov 18. doi: 10.1111/gean.12307.

Abstract

The emergence of the novel SARS-CoV-2 coronavirus and the global COVID-19 pandemic in 2019 led to explosive growth in scientific research. Alas, much of the research in the literature lacks conditions to be reproducible, and recent publications on the association between population density and the basic reproductive number of SARS-CoV-2 are no exception. Relatively few papers share code and data sufficiently, which hinders not only verification but additional experimentation. In this article, an example of reproducible research shows the potential of spatial analysis for epidemiology research during COVID-19. Transparency and openness means that independent researchers can, with only modest efforts, verify findings and use different approaches as appropriate. Given the high stakes of the situation, it is essential that scientific findings, on which good policy depends, are as robust as possible; as the empirical example shows, reproducibility is one of the keys to ensure this.

摘要

2019年新型严重急性呼吸综合征冠状病毒2(SARS-CoV-2)的出现以及全球新冠疫情的爆发,导致科研呈爆发式增长。遗憾的是,文献中的许多研究缺乏可重复性条件,近期关于人口密度与SARS-CoV-2基本繁殖数之间关联的出版物也不例外。相对较少的论文充分共享代码和数据,这不仅阻碍了验证,也妨碍了进一步的实验。在本文中,一个可重复性研究的例子展示了空间分析在新冠疫情期间流行病学研究中的潜力。透明度和开放性意味着独立研究人员只需付出适度努力,就能验证研究结果并酌情采用不同方法。鉴于形势 stakes 极高,良好政策所依赖的科学发现必须尽可能稳健;正如实证例子所示,可重复性是确保这一点的关键之一。

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