The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Quantitative assessment of the risk of airborne transmission of SARS-CoV-2 infection: Prospective and retrospective applications
Open accessPublished version of a preprint
Abstract
Airborne transmission is a recognized pathway of contagion; however, it is rarely quantitatively evaluated. The numerous outbreaks that have occurred during the SARS-CoV-2 pandemic are putting a demand on researchers to develop approaches capable of both predicting contagion in closed environments (predictive assessment) and analyzing previous infections (retrospective assessment). This study presents a novel approach for quantitative assessment of the individual infection risk of susceptible subjects exposed in indoor microenvironments in the presence of an asymptomatic infected SARS-CoV-2 subject. The application of a Monte Carlo method allowed the risk for an exposed healthy subject to be evaluated or, starting from an acceptable risk, the maximum exposure time. We applied the proposed approach to four distinct scenarios for a prospective assessment, highlighting that, in order to guarantee an acceptable risk of 10−3 for exposed subjects in naturally ventilated indoor environments, the exposure time could be well below one hour. Such maximum exposure time clearly depends on the viral load emission of the infected subject and on the exposure conditions; thus, longer exposure times were estimated for mechanically ventilated indoor environments and lower viral load emissions. The proposed approach was used for retrospective assessment of documented outbreaks in a restaurant in Guangzhou (China) and at a choir rehearsal in Mount Vernon (USA), showing that, in both cases, the high attack rate values can be justified only assuming the airborne transmission as the main route of contagion. Moreover, we show that such outbreaks are not caused by the rare presence of a superspreader, but can be likely explained by the co-existence of conditions, including emission and exposure parameters, leading to a highly probable event, which can be defined as a “superspreading event”.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Laboratory, field, modelling, and case studies have shown that airborne transmission through the inhalation of a virus-laden aerosol is important, if not dominant, for COVID-19.119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132”
From The Lancet Commission on lessons for the future from the COVID-19 pandemic (2022) · cites this paper as [126]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1
van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., Tamin, A., Harcourt, J. L., Thornburg, N. J., Gerber, S. I., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2004973 · Cited by 9,951
Virological assessment of hospitalized patients with COVID-2019
Wölfel, R., Corman, V. M., Guggemos, W., Seilmaier, M., Zange, S., Müller, M. A., Niemeyer, D., Jones, T. C., Vollmar, P., Rothe, C., et al. (2020) · Nature · doi:10.1038/s41586-020-2196-x · Cited by 6,661
Transmission of 2019-nCoV Infection from an Asymptomatic Contact in Germany
Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., Zimmer, T., Thiel, V., Jänke, C., Guggemos, W., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2001468 · Cited by 4,211
Hammersley, J. M., & Handscomb, D. C. (1964) · Springer eBooks · doi:10.1007/978-94-009-5819-7 · Cited by 4,175
To, K. K., Tsang, O. T.-Y., Leung, W.-S., Tam, A. R., Wu, T.-C., Lung, D. C., Yip, C. C., Cai, J., Chan, J. M., Chik, T. S., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30196-1 · Cited by 3,045
Airborne transmission of SARS-CoV-2: The world should face the reality
Morawska, L., & Cao, J. (2020) · Environment International · doi:10.1016/j.envint.2020.105730 · Cited by 1,728
Self-citationViral load of SARS-CoV-2 in clinical samples.
Pan, Y., Zhang, D., Yang, P., Poon, L. L. M., & Wang, Q. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30113-4 · Cited by 1,562
Morawska, L., Johnson, G. R., Ristovski, Z. D., Hargreaves, M., Mengersen, K., Corbett, S., Chao, C. Y. H., Li, Y., & Katoshevski, D. (2009) · Journal of Aerosol Science · doi:10.1016/j.jaerosci.2008.11.002 · Cited by 1,129 · cited 4×
“…1), followed by the oral breathing condition during heavy activity as the inhalation rate increases (2.5 quanta h− 1), and reaching 5.0 quanta h− 1 for the increase in aerosol emitted during vocalization (Morawska et al., 2009) and, finally, peaking during singing/speaking loudly (32 quanta h− 1).”
BackgroundInfluentialSelf-citationStadnytskyi, V., Bax, C. E., Bax, A., & Anfinrud, P. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2006874117 · Cited by 1,104 · cited 2×
“In the present paper we derived the droplet volume concentration on the basis of the total volume emitted by a loudspeaking person provided by (Stadnytskyi et al., 2020) as they measured the droplet volume taking into account the droplet dehydration.”
Chao, C. Y. H., Wan, M. P., Morawska, L., Johnson, G. R., Ristovski, Z., Hargreaves, M. H., Mengersen, K. L., Corbett, S. J., Li, Y., Xie, X., et al. (2008) · Journal of Aerosol Science · doi:10.1016/j.jaerosci.2008.10.003 · Cited by 1,090
Self-citation
Research works printed but not linked: 2
- Jennison, 1942, Atomizing of mouth and nose secretions into the air as revealed by high speed photography, Aerobiology, 17, 106
- World Health Organization, 2011. Guidelines for Drinking-water Quality - 4th Edition. WHO Regional Office for Europe.
Cited by
Sorted by citations
Items 401–446 of 446
SHIMASAKI, N. (2023) · Journal of Microorganism Control · doi:10.4265/jmc.28.4_177
RASHMEE, F. (2020) · The Maldives National Journal of Research · doi:10.62338/ebdzdp73
Ramadhan, F., Ayou, D. S., Prieto, J., & Coronas, A. (2024) · AIP conference proceedings · doi:10.1063/5.0188283
Aganović, A., Cao, G., Kurnitski, J., & Wargocki, P. L. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111494
STUDY ON INFECTION RISK ASSESSMENT METHODS IN LIVING SPACE
Shoji, K., & Kato, S. (2024) · Journal of Environmental Engineering (Transactions of AIJ) · doi:10.3130/aije.89.251
One-year operation performance of a decentralised all-air HVAC system for a school room
Noro, M., Busato, F., & Piterà, L. A. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202452302005
“As they are among the most densely occupied environments with continuous occupation, in recent years many studies focused on demonstrating the correlation between increased ventilation and reduced risk of contagion [8-10].”
BackgroundKakeshpour, T., & Bax, A. (2024) · Atmosphere · doi:10.3390/atmos15050570
CFD Assessment of COVID-19 Infection Risk in Naturally Ventilated Detached Houses
Tantasavasdi, C., Rianngon, T., & Inprom, N. (2024) · Nakhara Journal of Environmental Design and Planning · doi:10.54028/nj202423408
Trende, R., Darling, T. L., Gan, T., Wang, D., & Boon, A. C. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.08.597602
“Duration of exposure to a SARS-CoV-2 infected individual has been associated with higher infection rates in both humans 51 and animal models 52 .”
BackgroundIndoor transmission of the SARS-CoV-2 virus through aerosols emitted by e-cigarettes
Sussman, R. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95646-8.00036-6
Digital twin of ventilation system against COVID-19 transmission and infection risk
Tan, J. K. N., & Law, A. W. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95646-8.00045-7
Çetin, H. M., Özgünler, M., & Arpacıoğlu, Ü. (2024) · Mimarlık Bilimleri ve Uygulamaları Dergisi (MBUD) · doi:10.30785/mbud.1429762
Agent models for disease propagation
Genari, J. (2024) · doi:10.11606/d.55.2024.tde-29072024-155156
Vamsi, B., Jha, N. K., Dhariwal, J., Saran Raj, K., & Srirangarajan, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4978931
Clean Lift: a Case Study from Industrial to Civil Application
Romano, F., Pedranzini, F., & Motta, M. (2024) · IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) · doi:10.1109/eeeic/icpseurope61470.2024.10751313
“Numerous studies have investigated the risks associated with breathing, particularly focusing on how far respiratory particles can travel in the air, especially in enclosed spaces [1], [10], [11], [5].”
García, G., Acosta, I. J., Campano, M. Á., & Bustamante, P. (2025) · Springer proceedings in earth and environmental sciences · doi:10.1007/978-981-96-1270-3_2
Shinohara, N., Tatsu, K., Naito, W., Murashima, Y., Sakurai, H., Kizu, T., & Kan, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5119483
Análise da ventilação natural em um CIAC
Silva, A. D. N., Xavier, A. C. D. A., & Lukiantchuki, M. A. (2025) · PARC Pesquisa em Arquitetura e Construção · doi:10.20396/parc.v16i00.8675735
Methodology of Epidemic Risk Analysis in the Naval Military
Peultier-Celli, L., Gérard, A., Letourneur, F., Inghels, C., Duclos, A., & Perrin, P. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22040572
“For SARS-CoV-2, the emission rate was assessed as varying between 1 quanta · h − 1 and 100 quanta · h − 1 by measuring the viral concentration in saliva [23], or between 0.37 quanta · h − 1 and 32 quanta · h − 1 using Monte-Carlo simulations [29].”
InfluentialA multi-scale agent-based model of aerosol-mediated indoor infections in heterogeneous scenarios
Lanzarotti, E., Rojas, A. L. P., Roslan, F., Groisman, L., Santi, L., & Castro, R. (2025) · Journal of Simulation · doi:10.1080/17477778.2025.2476456
“…ways to estimate the airborne probability of infection in SARS-CoV-2 (Aganovic et al., 2021; Azimi et al., 2021; Buonanno, Morawska, et al., 2020; Buonanno, Stabile, et al., 2020; Katal et al., 2022; Kurnitski et al., 2021; Noakes et al., 2006; Peng et al., 2022; Vecherin et al., 2022; Yan et…”
Zhang, Y., Hui, F. K. P., Duffield, C. F., & Gao, C. X. (2025) · Facilities · doi:10.1108/f-07-2024-0093 · Cited by 1
Szałański, P., & Cepiński, W. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5377090
Preventive measures against infections, including viruses in the workplace
Morimoto, Y., Higashi, H., Izumi, H., Tomonaga, T., Nishida, C., Yamato, H., Eguchi, H., Kawanami, S., Suzuki, K., & Yatera, K. (2025) · SANGYO EISEIGAKU ZASSHI · doi:10.1539/sangyoeisei.2025-018-a
Wargocki, P. L., Theou-Tsafara, A., Psomas, T., & Aganović, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5429133
Numerical Simulation of Contaminant Transport and Infection Probability in Public Transport Vehicles
Fernández, J. V., Schillaci, E., Amani, A., Morozova, N., & Rigola, J. (2025) · Indoor Air · doi:10.1155/ina/5662076
“This work uses empirical data from di ff erent sources to estimate the quanta generation rate [33, 34].”
AI-Enhanced Technology Solutions for Improving Public Health Management and Disease Prevention
Kumar, V. (2025) · World Skills Conference on Universal Data Analytics and Sciences (WorldSUAS) · doi:10.1109/worldsuas66815.2025.11199117
“AI provides new opportunities to make better data-driven decisions, reduce reaction times, increase diagnostic accuracy, and maximize health resources as global health systems struggle to manage a growing population [2].”
Real-time direct measurement of indoor respiratory aerosols
Liu, M., Liu, X., Jiang, X., Quan, L., Zhang, R., Lu, X., & Wang, X. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.113891
Pereira, M. L., Vilain, R., & Tribess, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5679530
Li, H., Yin, R., Liu, M., Kong, X., Fan, M., & Jia, J. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.114515
On the fluid dynamics of toilet plume bioaerosols: An experimental study based on flow visualization
Peña, F. L., Gosset, A., & Lema, M. (2025) · International Symposium on Turbulence, Heat and Mass Transfer (THMT) · doi:10.1615/thmt-25.1330
Insights from a Ventilation-Aware Pandemic and Outbreak Risk model (VAPOR)
Wilson, N. J., Moore, C., Lee, E., Tuite, A. R., & Fisman, D. N. (2025) · Epidemics · doi:10.1016/j.epidem.2025.100878
Mikszewski, A., Stabile, L., Buonanno, G., & Morawska, L. (2021) · Figshare · doi:10.6084/m9.figshare.17086938
Self-citationSzajek, K., Fleisch, F., Hutter, S., Risch, M., Bechmann, T., Luyckx, V. A., Güsewell, S., Hirzel, C., Cusini, A., & Group, A. S. (2022) · Figshare · doi:10.6084/m9.figshare.18132261
Analysis of the input parameters for the infection risk model from CO 2 measurements
Mäkinen, A., Uusitalo, S., Juvela, J.-P., & Saari, S. (2025) · E3S Web of Conferences · doi:10.1051/e3sconf/202567207003
Stabile, L., Grossi, G., Caracci, E., Arpino, F., Cortellessa, G., & Buonanno, G. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114390
Self-citationLiu, Y., Huang, Z., Luo, X., & Ma, H. (2026) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2026.107724
Nehr, S., Baus, L., & Çınar, H. (2026) · Indoor Environments · doi:10.1016/j.indenv.2026.100176
Paddy, E. N., Sohail, M., & Afolabi, O. O. D. (2026) · Risk Analysis · doi:10.1111/risa.70260
“…from animal data (Armstrong and Haas 2007 ; Bartrand et al. 2008 ; Haas et al. 2000 ; R. M. Jones and Su 2015 ), including mouse (Watanabe et al. 2010 ), have been applied in human risk assessment studies (Armstrong and Haas 2007 ; Bartrand et al. 2008 ; Buonanno et al. 2020 ; Rowe et al. 2021 ).”
Dynamics of infectious disease spread between transportation hubs and surrounding communities
Mosleh, R., Shafadeh, M., Majeed, B. J., Arino, J., Ghasemi, A., Thommes, E. W., Asgary, A., Hui, H., & Wu, J. (2026) · Infectious Disease Modelling · doi:10.1016/j.idm.2026.05.009
“As has been shown in previous studies (Buonanno et al., 2020; Liu et al., 2017; Setti et al., 2020), diseases can easily spread without direct physical contact in transportation hubs.”
Natural ventilation performance for airborne diseases management in a deployable treatment facility
Fontana, L., Dell’Isola, M., Grossi, G., Silenzi, A., Marco, M. D., Chaillon, A., Wunderlich, R., & Buonanno, G. (2026) · Infection Control and Hospital Epidemiology · doi:10.1017/ice.2026.10508
“Infection risk, defined as the probability that a susceptible occupant becomes infected through inhalation of infectious particles, was estimated using the quanta-based approach previously described 13 and validated, 14,15 applying the Wells – Riley framework 16 to a single IDTM patient room.”
Self-citationThe Wells–Riley Model Revisited II: Parameter Uncertainty and Population Heterogeneity
Marshall, M., Edwards, A. J., Pinnell, D., King, M., Noakes, C. J., Elaldı, N., Lythe, G., & López‐García, M. (2026) · Risk Analysis · doi:10.1111/risa.70272
“…of factors, including: pathogen of interest (Mikszewski et al. 2022 ; Chen et al. 2006 ), differences in physical activity (Jones et al. 2024 ; Buonanno, Morawska, et al. 2020 ; Aganovic et al. 2023 ), the stage of infection (Cheng et al. 2025 ; Ferretti et al. 2020 ; Sender et al. 2022 ;…”
Liu, D., Zhao, J., Hussain, F., Wu, Y., Liu, J., Gao, F., Fan, Y., & Xu, D. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.115153
Beyond COVID-19: reconsidering routine contact precautions for common respiratory viruses
Bayhan, G. İ. (2026) · Antimicrobial Stewardship & Healthcare Epidemiology · doi:10.1017/ash.2026.10835
Theou-Tsafara, A., Psomas, T., Aganović, A., & Wargocki, P. L. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.118335
Zhao, F., Li, N., Lin, X., Gu, J., Dou, Z., Yu, H., Du, L., Deng, L., Xiao, S., & Zhang, N. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.115364
Metadata
- Authors
- Buonanno, G., Morawska, L., & Stabile, L.
- Affiliations
- Queensland University of Technology (AU)
- Università degli studi di Cassino e del Lazio Meridionale (IT)
- Corresponding author
- Stabile, L.
- Venue
- Environment International
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 145
- Pages
- 106112
- ISSN
- 0160-4120 (ISSN-L)
- PMC
- PMC7474922
- OpenAlex
- W3083235507
- Cited by
- 449
- References
- 46
- Citations by year
- 2026: 20 · 2025: 48 · 2024: 55 · 2023: 78 · 2022: 96 · 2021: 117 · 2020: 35
- FWCI
- 45.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Environment International (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Archivio Istituzionale della Ricerca - Università degli Studi di Cassino (submittedVersion)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Infection Control and Ventilation
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Physical Sciences › Environmental Science › Global and Planetary Change › COVID-19 impact on air quality
- Keywords
- SARS-CoV-2, airborne transmission, infection risk assessment, indoor microenvironments, asymptomatic infection, exposure time, superspreading events, superspreaders, outbreak investigation, Monte Carlo simulation, attack rate
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Aerosols, China, Humans, Pneumonia, Viral, Prospective Studies, Retrospective Studies, Air Pollution, Indoor, Coronavirus, Coronavirus Infections, Risk Assessment, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
