COVID 时代传染病模型的可重复性

IF 3 3区 医学 Q2 INFECTIOUS DISEASES Epidemics Pub Date : 2024-01-23 DOI:10.1016/j.epidem.2024.100743
Alec S. Henderson , Roslyn I. Hickson , Morgan Furlong , Emma S. McBryde , Michael T. Meehan
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引用次数: 0

摘要

在 COVID-19 大流行期间,传染病建模一直很受关注,它有助于了解病毒的传播动态并为应对政策提供依据。鉴于其潜在的重要性和转化影响,我们评估了 COVID 时代传染病模型文章的计算可重复性。我们发现,在 2020 年 1 月至 2022 年 8 月间发布的 100 篇随机抽样研究中,有 4 篇可以利用所提供的资源(如代码、数据、说明)进行完全计算重现,另有 8 篇可部分重现。在同期引用率最高的 100 篇文章中,我们发现有 11 篇可完全重现,另有 22 篇可部分重现。根据我们的经验,我们讨论了影响计算可重复性的常见问题以及如何解决这些问题。
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Reproducibility of COVID-era infectious disease models

Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus’ transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease modelling articles from the COVID era. We found that four out of 100 randomly sampled studies released between January 2020 and August 2022 could be completely computationally reproduced using the resources provided (e.g., code, data, instructions) whilst a further eight were partially reproducible. For the 100 most highly cited articles from the same period we found that 11 were completely reproducible with a further 22 partially reproducible. Reflecting on our experience, we discuss common issues affecting computational reproducibility and how these might be addressed.

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来源期刊
Epidemics
Epidemics INFECTIOUS DISEASES-
CiteScore
6.00
自引率
7.90%
发文量
92
审稿时长
140 days
期刊介绍: Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.
期刊最新文献
Estimating the generation time for influenza transmission using household data in the United States. Reconstructing the first COVID-19 pandemic wave with minimal data in England. Retrospective modelling of the disease and mortality burden of the 1918-1920 influenza pandemic in Zurich, Switzerland. Flusion: Integrating multiple data sources for accurate influenza predictions. Infectious diseases: Household modeling with missing data.
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