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Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment

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  • Small, Michael
  • Tse, C.K.

Abstract

We propose a new four state model for disease transmission and illustrate the model with data from the 2003 SARS epidemic in Hong Kong. The critical feature of this model is that the community is modelled as a small-world network of interconnected nodes. Each node is linked to a fixed number of immediate neighbors and a random number of geographically remote nodes. Transmission can only propagate between linked nodes. This model exhibits two features typical of SARS transmission: geographically localized outbreaks and “super-spreaders”. Neither of these features are evident in standard susceptible-infected-removed models of disease transmission. Our analysis indicates that “super-spreaders” may occur even if the infectiousness of all infected individuals is constant. Moreover, we find that nosocomial transmission in Hong Kong directly contributed to the severity of the outbreak and that by limiting individual exposure time to 3–5 days the extent of the SARS epidemic would have been minimal.

Suggested Citation

  • Small, Michael & Tse, C.K., 2005. "Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 351(2), pages 499-511.
    • Handle: RePEc:eee:phsmap:v:351:y:2005:i:2:p:499-511
    DOI: 10.1016/j.physa.2005.01.009
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    References listed on IDEAS

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    1. Tsimring, Lev S & Huerta, Ramón, 2003. "Modeling of contact tracing in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 325(1), pages 33-39.
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    Cited by:

    1. Choujun Zhan & Chi K Tse & Yuxia Fu & Zhikang Lai & Haijun Zhang, 2020. "Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.

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