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A Probabilistic Transmission Dynamic Model to Assess Indoor Airborne Infection Risks

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  • Chung‐Min Liao
  • Chao‐Fang Chang
  • Huang‐Min Liang

Abstract

The purpose of this article is to quantify the public health risk associated with inhalation of indoor airborne infection based on a probabilistic transmission dynamic modeling approach. We used the Wells‐Riley mathematical model to estimate (1) the CO2 exposure concentrations in indoor environments where cases of inhalation airborne infection occurred based on reported epidemiological data and epidemic curves for influenza and severe acute respiratory syndrome (SARS), (2) the basic reproductive number, R0 (i.e., expected number of secondary cases on the introduction of a single infected individual in a completely susceptible population) and its variability in a shared indoor airspace, and (3) the risk for infection in various scenarios of exposure in a susceptible population for a range of R0. We also employ a standard susceptible‐infectious‐recovered (SIR) structure to relate Wells‐Riley model derived R0 to a transmission parameter to implicate the relationships between indoor carbon dioxide concentration and contact rate. We estimate that a single case of SARS will infect 2.6 secondary cases on average in a population from nosocomial transmission, whereas less than 1 secondary infection was generated per case among school children. We also obtained an estimate of the basic reproductive number for influenza in a commercial airliner: the median value is 10.4. We suggest that improving the building air cleaning rate to lower the critical rebreathed fraction of indoor air can decrease transmission rate. Here, we show that virulence of the organism factors, infectious quantum generation rates (quanta/s by an infected person), and host factors determine the risk for inhalation of indoor airborne infection.

Suggested Citation

  • Chung‐Min Liao & Chao‐Fang Chang & Huang‐Min Liang, 2005. "A Probabilistic Transmission Dynamic Model to Assess Indoor Airborne Infection Risks," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1097-1107, October.
    • Handle: RePEc:wly:riskan:v:25:y:2005:i:5:p:1097-1107
    DOI: 10.1111/j.1539-6924.2005.00663.x
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    References listed on IDEAS

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    1. Raymond Gani & Steve Leach, 2001. "Transmission potential of smallpox in contemporary populations," Nature, Nature, vol. 414(6865), pages 748-751, December.
    2. Gwangpyo Ko & Kimberly M. Thompson & Edward A. Nardell, 2004. "Estimation of Tuberculosis Risk on a Commercial Airliner," Risk Analysis, John Wiley & Sons, vol. 24(2), pages 379-388, April.
    3. Mark Nicas & Alan Hubbard, 2002. "A Risk Analysis for Airborne Pathogens with Low Infectious Doses: Application to Respirator Selection Against Coccidioides immitis Spores," Risk Analysis, John Wiley & Sons, vol. 22(6), pages 1153-1163, December.
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    1. Gao, Qingwu & Zhuang, Jun, 2020. "Stability analysis and control strategies for worm attack in mobile networks via a VEIQS propagation model," Applied Mathematics and Computation, Elsevier, vol. 368(C).
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    3. Xie, Meiling & Li, Yuhan & Feng, Minyu & Kurths, Jürgen, 2023. "Contact-dependent infection and mobility in the metapopulation SIR model from a birth–death process perspective," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    4. Yakubenko, Slava, 2021. "Home alone? Effect of weather-induced behaviour on spread of SARS-CoV-2 in Germany," Economics & Human Biology, Elsevier, vol. 42(C).

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