key: cord-0528252-pb9ot0iw authors: Saqr, Khalid M. title: Amicus Plato, sed magis amica veritas: There is a reproducibility crisis in COVID-19 Computational Fluid Dynamics studies date: 2021-01-13 journal: nan DOI: nan sha: ece51fc447ffb955ac27a5e2cd61fa4ae72f86d5 doc_id: 528252 cord_uid: pb9ot0iw 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 $(R^{CFD}_jgeq 0.9)$ 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. To the date of writing this article, COVID-19 pandemic outbreak has resulted in 1.6 million deaths and a historic crash of financial markets [1] leading to major fractures in the world economy [2] . This pandemic challenges our healthcare systems, economic models and social lifestyle [3] . Many countries refuged to enforcing nationwide curfew [4] , travel ban and quarantine [5] , social distancing and obligatory use of face masks [6] as measures to mitigate the outbreak. Despite few earlier controversies [7, 8] , now there is a widely accepted theory among scientists now proposing that airborne transmission (AT) is a major infection scenario of COVID-19 [9] [10] [11] . Few days after the WHO declared COVID-19 a pandemic [12] , a study [13] published in New England Journal of Medicine demonstrated the possibility of SARS-CoV-2 AT experimentally. The study compared the stability of SARS-CoV-1 and SARS-CoV-2 in aerosols and on surfaces. It was evidently shown that SARS-CoV-2 has an aerosol stability similar to SARS-CoV-1 and remains infectious in aerosols for hours. SARS-CoV-2 infected patients were shown to exhibit high viral loads in the upper-respiratory tract [14] , manifesting the possibility of producing highly infectious aerosols even from asymptomatic patients [15] . Infectious aerosols are often categorized according to the droplet particle size. During a sneeze or a cough, aerosols of respiratory tract fluid are produced containing large particles (i.e. droplets) typically greater than 5 µm in diameter. These particles impact directly on a susceptible individual. On the other hand, a susceptible individual could possibly inhale microscopic aerosol particles consisting of the residual solid components of evaporated respiratory droplets, which are small enough (<5 µm) to remain airborne for hours [16] . Even during speech, thousands of oral fluid droplets that constitute AT and COVID-19 infection risk [17] . It was also established that infectious SARS-CoV-2 RNA is persistent in aerosols collected in the vicinity of infected individuals with particles of small and large sizes [18] . Jin et al [19] showed that SARS-CoV-2 positive air samples can be collected in ICU room for four days after the residing patient tested negative. Guo et al [20] collected positive samples from ICU air as far as 4 m from patients. Despite rapid air changing in airborne infection isolation rooms (AIIRs), Chia et al [21] showed that SARS-CoV-2 RNA can be detected in air samples with particle sizes of > 4 and 1 − 4 . Razzini et al [22] confirmed the persistence of SARS-CoV-2 RNA in air samples taken from ICU room and corridor of a hospital in Milan, Italy. In highly populated communities and crowded spaces, AT could lead to catastrophic rise in infection probability [23, 24] . Therefore, investigating AT aerodynamics is of eminent importance to help mitigate infection risk at different scales and scenarios [25] . Computational Fluid Dynamics (CFD) is a very useful tool to manifest such importance by providing rapid evaluation method to identify AT risk at virtually any given scenario [26] . SARS-CoV-2 is an enveloped virus with a diameter of 0.1 , aerosol half-life of 1 hour and a concentration of 10 4 − 10 11 RNAs/mL in respiratory fluids [27] . These are typical numbers for the Coronaviridae [28] , including SARS-CoV-1 that caused the first pandemic of the 21 st century. It has been established that viral RNA is carried in fluid particles produced by symptomatic patients while coughing and breathing [29] leading to AT of the virus. The degree to which AT constitutes infection risk depends on many variables that are yet to be comprehensively understood. The pathogenic similarity between the SARS-CoV-1 and SARS-CoV-2 [30] establishes relevance between the role of AT in the two corresponding pandemics. The studies conducted on the 2002 SARS outbreak in Amoy Gardens housing complex of Hong Kong presented important insights. Yu et al [31] studied the distribution of the initial 187 cases of SARS in Hong Kong while searching for a possible transmission pathway to justify that cluster infection. Using logistic regression and CFD simulations, they showed that the infection risk pattern corresponded well with the predicted aerosol transfer pattern between apartments. In another study, Chu et al [32] analyzed the correlation between nasopharyngeal viral load of The purpose of this article is to promote better reporting practice of CFD studies related to COVID-19 research and biomedical research at large. The scope is limited to the concept of reproducibility in CFD practice. The establishment of any CFD study requires proper level of verification, validation and reproducibility otherwise, it would not be possible to confirm the study's conclusion [35] . There are three criteria of reproducibility that any fluid dynamicist with firsthand experience in modern CFD software needs to address in order to replicate a simulation case [59, 60] . where is the study index and is the number of reproducibility elements, respectively. Equation (1) reproducibility possible (50% of the total weight per study). Two elements of low weight ( = 5) represent the information that would make the reproducibility process easy (10% of the total weight per study). Here, we argue that for a study to be reproducible ≥ 0.9. Irreproducible studies have ≤ 0.4 while studies with 0.4 < < 0.9 are difficult to reproduce as they lack information necessary to perform CFD simulation. Particle density 5 1 if the particle density was reported 0 if the particle density was not reported Aerosol particle diameter 5 1 if the particle diameter is reported 0 if the particle dimeter is not reported figure 3 . The distribution, however, is slightly skewed for < 0.62. Three studies, one from each set [42, 46, 57] , achieved full reproducibility score ( ≥ 0.9) while six studies [40, 41, 44, 47, 52, 53] were found to be irreproducible The remaining 14 studies were found to have a reproducibility score in the range of 0.4 < < 0.9. These studies are difficult to reproduce as they lack important information. The difficulty of reproducing these studies varies according to the missing information. Figure 4 shows the missing and available information in the 14 studies according to the elements presented in table 2 . Missing information about Reynolds number and aerosol particle diameter characterize 79% and 36% of these studies, respectively. Missing dimensions and validation information characterize 50% of such studies while the remaining reproducibility elements vary from one study to the other. Infection control in healthcare facilities is of crucial importance in managing COVID-19 outbreak. Set (A) [36] [37] [38] [39] [40] [41] [42] [43] comprises CFD studies of different AT scenarios in hospitals and healthcare facilities. The computational domain in these studies always represent the air flow around aerosol source of particular settings that represent SARS-CoV-2 AT. Grid resolution ranged from 0.9 × 10 6 in simple two-dimensional representation of generic care room [38] to 50 × 10 6 cells in three-dimensional representations in prefabricated inpatient ward [42] . The use of RANS models was the main approach to model turbulence with just two studies were reported using LES [38, 39] . Aerosol modeling was predominantly conducted using the Eulerian-Lagrangian approach [61] . Only one study reported the Reynolds number value [38] and another reported aerosol particle density [36] of the simulation. Four studies [36, [41] [42] [43] reported the aerosol particle size in the simulations with a range from 0.69 to 500 . Relatively similar approaches were identified in the studies comprising set (C) [53] [54] [55] [56] [57] [58] where SARS-CoV-2 AT was studied in generic building spaces. On the other hand, the respiratory studies comprised in set (B) [44] [45] [46] [47] [48] [49] [50] [51] [52] (C). These CFD studies should empower our understanding of COVID-19 outbreak [7, 26, 62, 63] . This is demonstrated by the citation data of these studies, as presented earlier in this article. However, and unlike other important testing and characterization tools addressing this unprecedented pandemic, published CFD studies suffer from a lack of sufficient reproducibility criteria to advance infectious aerosol research. With more than half of the studies missing information about domain dimensions, Reynolds number, particle density and validation; a reproducibility crisis is identified and should be addressed. It is noteworthy to mention that none of the studies included in this review has provided any form or format of digital files to enable manipulation and processing of the CFD results. A novel reproducibility index ( )is proposed to measure the possibility of a CFD study to be reproduced. The index accounts for three criteria comprising a total of 10 elements that comprise the essential information required for reproducing CFD simulation. When the index was applied to 23 published CFD studies related to COVID-19, it revealed a reproducibility crisis. The results were normally distributed around a mean value of 0.62 and revealed that only 13% of the selected studies achieve the reproducibility criteria( ≥ 0.9). In conclusion, we propose this novel reproducibility index as a criteria for publishing CFD studies related to COVID-19 research in order to empower reproducibility and validation in this important research topic. Financial markets under the global pandemic of COVID-19 Economic effects of coronavirus outbreak (COVID-19) on the world economy. Available at SSRN 3557504 COVID-19: Impact by and on the environment, health and economy COVID-19 lockdowns throughout the world. Occupational Medicine Covid-19-the law and limits of quarantine Face masks are new normal after COVID-19 pandemic Transmission of COVID-19 virus by droplets and aerosols: A critical review on the unresolved dichotomy Is the coronavirus airborne? Experts can't agree Airborne transmission of SARS-CoV-2: The world should face the reality Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals A Letter about the Airborne Transmission of SARS-CoV-2 Based on the Current Evidence Director-General's opening remarks at the media briefing on COVID-19. World Health Organization Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1 SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients Presumed Asymptomatic Carrier Transmission of COVID-19 The coronavirus pandemic and aerosols: Does COVID-19 transmit via expiratory particles? The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission The Infectious Nature of Patient-Generated SARS-CoV-2 Aerosol SARS-CoV-2 presented in the air of an intensive care unit (ICU) Aerosol and surface distribution of severe acute respiratory syndrome coronavirus 2 in hospital wards Detection of air and surface contamination by SARS-CoV-2 in hospital rooms of infected patients SARS-CoV-2 RNA detection in the air and on surfaces in the COVID-19 Identifying airborne transmission as the dominant route for the spread of COVID-19 Association of the infection probability of COVID-19 with ventilation rates in confined spaces Airborne transmission route of COVID-19: why 2 meters/6 feet of inter-personal distance could not Be enough The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control SARS-CoV-2 (COVID-19) by the numbers Chapter 17 -Family Coronaviridae Respiratory virus RNA is detectable in airborne and droplet particles Differences and similarities between SARS-CoV and SARS-CoV-2: spike receptor-binding domain recognition and host cell infection with support of cellular serine proteases Evidence of Airborne Transmission of the Severe Acute Respiratory Syndrome Virus Viral load distribution in SARS outbreak Multi-zone modeling of probable SARS virus transmission by airflow between flats in Block E Detection of airborne severe acute respiratory syndrome (SARS) coronavirus and environmental contamination in SARS outbreak units Data Without Software Are Just Numbers Impact of hvacsystems on the dispersion of infectious aerosols in a cardiac intensive care unit A novel CFD analysis to minimize the spread of COVID-19 virus in hospital isolation room Predicting airborne coronavirus inactivation by far-UVC in populated rooms using a high-fidelity coupled radiation-CFD model High-efficiency simulation framework to analyze the impact of exhaust air from covid-19 temporary hospitals and its typical applications Implementing a negative pressure isolation space within a skilled nursing facility to control SARS-CoV-2 transmission Minimising exposure to droplet and aerosolised pathogens: a computational fluid dynamics study Numerical Study of Three Ventilation Strategies in a prefabricated COVID-19 inpatient ward. Building and Environment A numerical study of ventilation strategies for infection risk mitigation in general inpatient wards A novel reusable anti-COVID-19 transparent face respirator with optimized airflow. Bio-Design and Manufacturing Numerical evaluation of spray position for improved nasal drug delivery Sneezing and asymptomatic virus transmission Transmission of droplet-conveyed infectious agents such as SARS-CoV-2 by speech and vocal exercises during speech therapy: preliminary experiment concerning airflow velocity Comparison of Voxel-Based and Mesh-Based CFD Models for Aerosol Deposition on Complex Fibrous Filters Fluid dynamics simulations show that facial masks can suppress the spread of COVID-19 in indoor environments Preliminary Findings on Control of Dispersion of Aerosols and Droplets During High-Velocity Nasal Insufflation Therapy Using a Simple Surgical Mask: Implications for the High-Flow Nasal Cannula The effect of oral and nasal breathing on the deposition of inhaled particles in upper and tracheobronchial airways Washing hands and the face may reduce COVID-19 infection The design of safe classrooms of educational buildings for facing contagions and transmission of diseases: A novel approach combining audits, calibrated energy models, building performance (BPS) and computational fluid dynamic (CFD) simulations Tracking the Flu Virus in a Room Mechanical Ventilation Using CFD Tools and Effective Disinfection of an HVAC System Can a toilet promote virus transmission? From a fluid dynamics perspective on the effect of the respiratory droplet generation condition on COVID-19 transmission Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors Risk assessment of airborne transmission of COVID-19 by asymptomatic individuals under different practical settings Reproducible and Replicable Computational Fluid Dynamics: It's Harder Than You Think Re-run, repeat, reproduce, reuse, replicate: transforming code into scientific contributions Detailed simulation of viral propagation in the built environment An overview of experiments and numerical simulations on airflow and aerosols deposition in human airways and the role of bioaerosol motion in covid-19 transmission Environmental factors affecting the transmission of respiratory viruses