柏拉图之友，真理之友：在COVID-19计算流体动力学研究中存在可重复性危机
Amicus Plato, sed magis amica veritas: There is a reproducibility crisis in COVID-19 Computational Fluid Dynamics studies
摘要：
在持续的COVID-19爆发中，有压倒性的证据表明SARS-CoV-2空气传播（AT）。然而，要推导出一个通用的框架来评估相对于其他模式的相对空气传播风险是非常困难的。这是由于这种现象所包含的复杂的生物物理学。自2002年SARS爆发以来，计算流体动力学（CFD）已成为研究呼吸道病毒AT的主要工具之一。现在，CFD模拟产生直观和物理上合理的颜色编码结果，帮助科学家了解SARS-CoV-2空气传播模式。除了验证要求外，任何CFD模型要对科学界具有认识价值，它必须是可再现的。2020年，超过45项已发表的研究使用CFD在不同场景下调查了SARS-CoV-2的空气传播。在这里，我系统地回顾了已发表的COVID-19的CFD研究，并讨论了CFD建模过程的再现性标准。利用加权评分模型（WSM），我提出了一个新的再现性指标的CFD模拟的SARS-CoV-2 AT。建议的指数$(0 \leq R^{CFD}_j \leq 1)$依赖于三个再现性标准，包括10个代表CFD研究（j）再现可能性的元素。令人沮丧的是，23项研究中只有3项（13%）达到了完全重现性指标$(R^{CFD}_j\geq 0.9)$，而其余87%的研究发现$(R^{CFD}_j<0.9)$通常不可重现。如果没有可重复的模型，CFD模拟的科学效益将仍然受到阻碍，分散和限制。总之，我呼吁科学界在COVID-19研究中采用更严格的措施来报告和发布CFD模拟。
英文摘要：
There is overwhelming evidence on SARS-CoV-2 Airborne Transmission (AT) in the ongoing COVID-19 outbreak. It is extraordinarily difficult, however, to deduce a generalized framework to assess the relative airborne transmission risk with respect to other modes. This is due to the complex biophysics entailed in such phenomena. Since the SARS outbreak in 2002, Computational Fluid Dynamics (CFD) has been one of the main tools scientists used to investigate AT of respiratory viruses. Now, CFD simulations produce intuitive and physically plausible colour-coded results that help scientists understand SARS-CoV-2 airborne transmission patterns. In addition to validation requirements, for any CFD model to be of epistemic value to the scientific community; it must be reproducible. In 2020, more than 45 published studies investigated SARS-CoV-2 airborne transmission in different scenarios using CFD. Here, I systematically review the published CFD studies of COVID-19 and discuss their reproducibility criteria with respect to the CFD modeling process. Using a Weighted Scoring Model (WSM), I propose a novel reproducibility index for CFD simulations of SARS-CoV-2 AT. The proposed index $(0 \leq R^{CFD}_j \leq 1)$ relies on three reproducibility criteria comprising 10 elements that represent the possibility of a CFD study (j) to be reproduced. Frustratingly, only 3 of 23 studies (13%) achieved full reproducibility index $(R^{CFD}_j\geq 0.9)$ while the remaining 87% were found generally irreproducible $(R^{CFD}_j<0.9)$. Without reproducible models, the scientific benefit of CFD simulations will remain hindered, fragmented and limited. In conclusion, I call the scientific community to apply more rigorous measures on reporting and publishing CFD simulations in COVID-19 research.
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