Skip to main content
PMC home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2020 Aug 4;13(6):1321–1327. doi: 10.1007/s12273-020-0703-5

Association of the infection probability of COVID-19 with ventilation rates in confined spaces

Hui Dai 1, Bin Zhao 1,2,✉
PMCID: PMC7398856  PMID: 32837691

Abstract

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.

Keywords: COVID-19, SARS-CoV-2, ventilation, infection probability, Wells-Riley equation

Acknowledgements

The study was supported by the Ministry of Science and Technology of China (No. 2020YFC0842500) and the National Natural Science Foundation of China (No. 52041602, No. 51521005).

References

  1. Anderson RM, Anderson B, May RM. Infectious Diseases of Humans: Dynamics and Control. Oxford, UK: Oxford University Press; 1992. [Google Scholar]
  2. Anfinrud P, Stadnytskyi V, Bax CE, Bax A. Visualizing speechgenerated oral fluid droplets with laser light scattering. New England Journal of Medicine. 2020;382:2061–2063. doi: 10.1056/NEJMc2007800. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Buonanno G, Stabile L, Morawska L. Estimation of airborne viral emission: Quanta emission rate of SARS-CoV-2 for infection risk assessment. Environment International. 2020;141:105794. doi: 10.1016/j.envint.2020.105794. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Chartier Y, Atkinson J, Pessoa-Silva C (2009). Natural ventilation for infection control in health-care settings. World Health Organization. [PubMed]
  5. D’Arienzo M, Coniglio A. Assessment of the SARS-CoV-2 basic reproduction number, R0, based on the early phase of COVID-19 outbreak in Italy. Biosafety and Health. 2020;2:57–59. doi: 10.1016/j.bsheal.2020.03.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Davies A, Thompson KA, Giri K, Kafatos G, Walker J, Bennett A. Testing the efficacy of homemade masks: would they protect in an influenza pandemic? Disaster Medicine and Public Health Preparedness. 2013;7:413–418. doi: 10.1017/dmp.2013.43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Duan X, Zhao X, Wang B, Chen Y, Cao S. Exposure Factors Handbook of Chinese Population (Adults) Beijing: China Environmental Science Press; 2013. [Google Scholar]
  8. Escombe AR, Oeser CC, Gilman RH, Navincopa M, Ticona E, et al. Natural ventilation for the prevention of airborne contagion. PLoS Medicine. 2007;4:e68. doi: 10.1371/journal.pmed.0040068. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Fennelly KP, Nardell EA. The relative efficacy of respirators and room ventilation in preventing occupational tuberculosis. Infection Control and Hospital Epidemiology. 1998;19:754–759. doi: 10.2307/30141420. [DOI] [PubMed] [Google Scholar]
  10. Fisk WJ, Seppanen O, Faulkner D, Huang J (2004). Economic benefits of an economizer system: Energy savings and reduced sick leave. Technical Report, LBNL-54475, Lawrence Berkeley National Laboratory.
  11. Foarde KK. Methodology to perform clean air delivery rate type determinations with microbiological aerosols. Aerosol Science and Technology. 1999;30:235–245. doi: 10.1080/713834074. [DOI] [Google Scholar]
  12. Guo Z-D, Wang Z-Y, Zhang S-F, Li X, Li L, et al. Aerosol and surface distribution of severe acute respiratory syndrome coronavirus 2 in hospital wards, Wuhan, China, 2020. Emerging Infectious Diseases. 2020;26:1583–1591. doi: 10.3201/eid2607.200885. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hui DS, Chow BK, Chu L, Ng SS, Lee N, et al. Exhaled air dispersion during coughing with and without wearing a surgical or N95 mask. PLoS One. 2012;7:e50845. doi: 10.1371/journal.pone.0050845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Imai N, Cori A, Dorigatti I, Baguelin M, Donnelly CA, et al. (2020). Report 3: Transmissibility of 2019-nCoV. Imperial College London. Available at http://hdl.handle.net/10044/1777148.
  15. Ji W, Zhao B. Contribution of outdoor-originating particles, indoor-emitted particles and indoor secondary organic aerosol (SOA) to residential indoor PM2.5 concentration: A model-based estimation. Building and Environment. 2015;90:196–205. doi: 10.1016/j.buildenv.2015.04.006. [DOI] [Google Scholar]
  16. Lee S, Golinski M, Chowell G. Modeling optimal age-specific vaccination strategies against pandemic influenza. Bulletin of Mathematical Biology. 2012;74:958–980. doi: 10.1007/s11538-011-9704-y. [DOI] [PubMed] [Google Scholar]
  17. Li HL, Zhang XL, Wang K. A quantitative study on the epidemic situation of tuberculosis based on the transmission disease dynamics in 14 prefectures of Xinjiang from 2005 to 2017. Chinese Journal of Infection Control. 2018;17(11):945–950. [Google Scholar]
  18. Li Q, Guan X, Wu P, Wang X, Zhou L, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. New England Journal of Medicine. 2020;382:1199–1207. doi: 10.1056/NEJMoa2001316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Liu Y, Gayle AA, Wilder-Smith A, Rocklöv J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine. 2020;27(2):taaa021. doi: 10.1093/jtm/taaa021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Liu Y, Ning Z, Chen Y, Guo M, Liu Y, et al. Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals. Nature. 2020;582:557–560. doi: 10.1038/s41586-020-2271-3. [DOI] [PubMed] [Google Scholar]
  21. Lu J, Gu J, Li K, Xu C, Su W, et al. COVID-19 outbreak associated with air conditioning in restaurant, Guangzhou, China, 2020. Emerging Infectious Diseases. 2020;26:1628–1631. doi: 10.3201/eid2607.200764. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Majumder M, Mandl KD (2020). Early transmissibility assessment of a novel coronavirus in Wuhan, China. SSRN Electronic Journal, 10.2139/ssrn.3524675. [DOI]
  23. Morawska L, Cao J. Airborne transmission of SARS-CoV-2: The world should face the reality. Environment International. 2020;139:105730. doi: 10.1016/j.envint.2020.105730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. National Research Council . Rapid Expert Consultation on the Possibility of Bioaerosol Spread of SARS-CoV-2 for the COVID-19 Pandemic. Washington DC: The National Academies Press; 2020. [Google Scholar]
  25. Plans Rubió P. Is the basic reproductive number (R0) for measles viruses observed in recent outbreaks lower than in the pre-vaccination era? Euro Surveillance. 2012;17(31):20233. doi: 10.2807/ese.17.31.20233-en. [DOI] [PubMed] [Google Scholar]
  26. Read JM, Bridgen JRE, Cummings DAT, Ho A, Jewell CP (2020). Novel coronavirus 2019-nCoV: Early estimation of epidemiological parameters and epidemic predictions. medRxiv, 10.1101/2020.01.23.20018549.
  27. Riley EC, Murphy G, Riley RL. Airborne spread of measles in a suburban elementary school. American Journal of Epidemiology. 1978;107:421–432. doi: 10.1093/oxfordjournals.aje.a112560. [DOI] [PubMed] [Google Scholar]
  28. Riou J, Althaus CL. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Euro Surveillance. 2020;25:2000058. doi: 10.2807/1560-7917.ES.2020.25.4.2000058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Sherman MH, Wilson DJ. Relating actual and effective ventilation in determining indoor air quality. Building and Environment. 1986;21:135–144. doi: 10.1016/0360-1323(86)90022-3. [DOI] [Google Scholar]
  30. Stadnytskyi V, Bax CE, Bax A, Anfinrud P. The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission. Proceedings of the National Academy of Sciences of the United States of America(PNAS) 2020;117:11875–11877. doi: 10.1073/pnas.2006874117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Stephens B (2012). HVAC filtration and the Wells-Riley approach to assessing risks of infectious airborne diseases. National Air Filtration Association (NAFA) Foundation Report.
  32. Tang B, Bragazzi NL, Li Q, Tang S, Xiao Y, Wu J. An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov) Infectious Disease Modelling. 2020;5:248–255. doi: 10.1016/j.idm.2020.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Van Doremalen N, Bushmaker T, Morris DH, Holbrook MG, Gamble A, et al. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. New England Journal of Medicine. 2020;382:1564–1567. doi: 10.1056/NEJMc2004973. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. WHO (2003). Consensus document on the epidemiology of severe acute respiratory syndrome (SARS) (No. WHO/CDS/CSR/GAR/2003.11). World Health Organization.
  35. WHO (2019). WHO MERS global summary and assessment of risk, July 2019 (No. WHO/MERS/RA/19.1). World Health Organization.
  36. Zhao B, Liu Y, Chen C. Air purifiers: A supplementary measure to remove airborne SARS-CoV-2. Building and Environment. 2020;177:106918. doi: 10.1016/j.buildenv.2020.106918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Zhao S, Lin Q, Ran J, Musa SS, Yang G, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. International Journal of Infectious Diseases. 2020;92:214–217. doi: 10.1016/j.ijid.2020.01.050. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Building Simulation are provided here courtesy of Nature Publishing Group

RESOURCES