Sy Karla Therese L, White Laura F, Nichols Brooke E
Department of Epidemiology Boston University School of Public Health Boston MA USA.
Department of Global Health Boston University School of Public Health Boston MA USA.
Geogr Anal. 2021 Nov 16. doi: 10.1111/gean.12314.
Reproducible research becomes even more imperative as we build the evidence base on SARS-CoV-2 epidemiology, diagnosis, prevention, and treatment. In his study, Paez assessed the reproducibility of COVID-19 research during the pandemic, using a case study of population density. He found that most articles that assess the relationship of population density and COVID-19 outcomes do not publicly share data and code, except for a few, including our paper, which he stated "illustrates the importance of good reproducibility practices". Paez recreated our analysis using our code and data from the perspective of spatial analysis, and his new model came to a different conclusion. The disparity between our and Paez's findings, as well as other existing literature on the topic, give greater impetus to the need for further research. As there has been near exponential growth of COVID-19 research across a wide range of scientific disciplines, reproducible science is a vital component to produce reliable, rigorous, and robust evidence on COVID-19, which will be essential to inform clinical practice and policy in order to effectively eliminate the pandemic.
随着我们建立关于严重急性呼吸综合征冠状病毒2(SARS-CoV-2)流行病学、诊断、预防和治疗的证据基础,可重复性研究变得更加迫切。在他的研究中,佩兹以人口密度为例,评估了大流行期间新型冠状病毒肺炎(COVID-19)研究的可重复性。他发现,除了少数几篇文章(包括我们的论文,他称其“说明了良好的可重复性实践的重要性”)外,大多数评估人口密度与COVID-19结果之间关系的文章都没有公开共享数据和代码。佩兹从空间分析的角度使用我们的代码和数据重新进行了我们的分析,他的新模型得出了不同的结论。我们的研究结果与佩兹的研究结果以及关于该主题的其他现有文献之间的差异,进一步推动了对进一步研究的需求。由于COVID-19研究在广泛的科学学科中几乎呈指数级增长,可重复性科学是产生关于COVID-19的可靠、严谨和有力证据的重要组成部分,这对于为临床实践和政策提供信息以有效消除大流行至关重要。