Development of a dose-response model for SARS coronavirus

Toru Watanabe, Timothy A. Bartrand, Mark H. Weir, Tatsuo Omura, Charles N. Haas

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


In order to develop a dose-response model for SARS coronavirus (SARS-CoV), the pooled data sets for infection of transgenic mice susceptible to SARS-CoV and infection of mice with murine hepatitis virus strain 1, which may be a clinically relevant model of SARS, were fit to beta-Poisson and exponential models with the maximum likelihood method. The exponential model (k = 4.1 × l02) could describe the dose-response relationship of the pooled data sets. The beta-Poisson model did not provide a statistically significant improvement in fit. With the exponential model, the infectivity of SARS-CoV was calculated and compared with those of other coronaviruses. The does of SARS-CoV corresponding to 10% and 50% responses (illness) were estimated at 43 and 280 PFU, respectively. Its estimated infectivity was comparable to that of HCoV-229E, known as an agent of human common cold, and also similar to those of some animal coronaviruses belonging to the same genetic group. Moreover, the exponential model was applied to the analysis of the epidemiological data of SARS outbreak that occurred at an apartment complex in Hong Kong in 2003. The estimated dose of SARS-CoV for apartment residents during the outbreak, which was back-calculated from the reported number of cases, ranged from 16 to 160 PFU/person, depending on the floor. The exponential model developed here is the sole dose-response model for SARS-CoV at the present and would enable us to understand the possibility for reemergence of SARS.

Original languageEnglish
Pages (from-to)1129-1138
Number of pages10
JournalRisk Analysis
Issue number7
Publication statusPublished - 2010 Jul


  • Dose-response model
  • SARS coronavirus
  • SARS outbreak in Hong Kong
  • maximum likelihood method

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Physiology (medical)

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