The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Association of the infection probability of COVID-19 with ventilation rates in confined spaces
Open accessAbstract
A growing number of cases have proved the possibility of airborne transmission of the coronavirus disease 2019 (COVID-19). Ensuring an adequate ventilation rate is essential to reduce the risk of infection in confined spaces. In this study, we estimated the association between the infection probability and ventilation rates with the Wells-Riley equation, where the quantum generation rate (q) by a COVID-19 infector was obtained using a reproductive number-based fitting approach. The estimated q value of COVID-19 is 14–48 h−1. To ensure an infection probability of less than 1%, a ventilation rate larger than common values (100–350 m3/h per infector and 1200–4000 m3/h per infector for 0.25 h and 3 h of exposure, respectively) is required. If the infector and susceptible person wear masks, then the ventilation rate ensuring a less than 1% infection probability can be reduced to a quarter respectively, which is easier to achieve by the normal ventilation mode applied in typical scenarios, including offices, classrooms, buses, and aircraft cabins. Strict preventive measures (e.g., wearing masks and preventing asymptomatic infectors from entering public spaces using tests) that have been widely adopted should be effective in reducing the risk of infection in confined spaces.
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.
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
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575
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
Infectious Diseases of Humans: Dynamics and Control
(1992) · Annals of Internal Medicine · doi:10.7326/0003-4819-117-2-174_4 · Cited by 7,746
Wu, J. T., Leung, K., & Leung, G. M. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30260-9 · Cited by 4,389
The reproductive number of COVID-19 is higher compared to SARS coronavirus
Liu, Y., Gayle, A. A., Wilder-Smith, A., & Rocklöv, J. (2020) · Journal of Travel Medicine · doi:10.1093/jtm/taaa021 · Cited by 3,192
Aerodynamic Analysis of SARS-CoV-2 in Two Wuhan Hospitals
Liu, Y., Ning, Z., Chen, Y., Guo, M., Liu, Y., Gali, N. K., Sun, L., Duan, Y., Cai, J., Westerdahl, D., et al. (2020) · Nature · doi:10.1038/s41586-020-2271-3 · Cited by 1,872
Zhao, S., Lin, Q., Ran, J., Musa, S. S., Yang, G., Wang, W., Lou, Y., Gao, D., Yang, L., He, D., et al. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.01.050 · Cited by 1,853
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
Infectious diseases of humans: Dynamics and control
Dye, C. (1991) · Trends in Ecology & Evolution · doi:10.1016/0169-5347(91)90048-3 · Cited by 1,475
Riou, J., & Althaus, C. L. (2020) · Eurosurveillance · doi:10.2807/1560-7917.es.2020.25.4.2000058 · Cited by 1,297
Cited by
Probable airborne transmission of SARS-CoV-2 in a poorly ventilated restaurant
Li, Y., Qian, H., Hang, J., Chen, X., Cheng, P., Ling, H., Wang, S., Liang, P., Li, J., Xiao, S., et al. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.107788 · Cited by 689
Ou, C., Hu, S., Luo, K., Yang, H., Hang, J., Cheng, P., Hai, Z., Xiao, S., Qian, H., Xiao, S., et al. (2022) · Building and Environment · doi:10.1016/j.buildenv.2021.108414 · Cited by 110
Metadata
- Authors
- Dai, H., & Zhao, B.
- Venue
- Building Simulation
- Published
- 2020-08-04
- Volume
- 13
- Issue
- 6
- Pages
- 1321-1327
- ISSN
- 1996-3599 (ISSN-L)
- 1996-8744
- OpenAlex
- W3046489363
- Cited by
- 409
- References
- 33
- FWCI
- 36.33
- Type
- journal article
- Language
- en
- Open access
- bronze
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Association of the infection probability of COVID-19 with ventilation rates in confined spaces. https://app.cochat.ai/papers/10.1007/s12273-020-0703-5
- The paper
- Dai, H., & Zhao, B. (2020). Association of the infection probability of COVID-19 with ventilation rates in confined spaces. Building Simulation. https://doi.org/10.1007/s12273-020-0703-5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
