  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.
