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Reproducible Science Is Vital for a Stronger Evidence Base During the COVID‐19 Pandemic
Author(s) -
Sy Karla Therese L.,
White Laura F.,
Nichols Brooke E.
Publication year - 2023
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12314
Subject(s) - pandemic , covid-19 , perspective (graphical) , population , data science , psychology , computer science , medicine , sociology , demography , disease , pathology , infectious disease (medical specialty) , artificial intelligence
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.