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Volume 12, Number 1—January 2006

Research

Real-time Estimates in Early Detection of SARS

Simon Cauchemez*†Comments to Author , Pierre-Yves Boëlle*†‡, Christl A. Donnelly§, Neil M Ferguson§, Guy Thomas*†‡, Gabriel M. Leung¶, Anthony J Hedley¶, Roy M. Anderson§, and Alain-Jacques Valleron*†‡
Author affiliations: *Institut National de la Santé et de la Recherche Médicale, Paris, France; †Université Pierre et Marie Curie, Paris, France; ‡Assistance Publique–Hôpitaux de Paris, Paris, France; §Imperial College, London, United Kingdom; ¶University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China

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Figure 2

Average expectation of the temporal pattern of Rt after implementation of control measures according to the day T of the last observation. A) Completely effective control measures. B) Limited control measures. Simulation values of R are also given: before day 20, R = 3; after day 20 R = 0 (A) and R = 0.7 (B). The gray zone indicates that R is <1. Information that the average expectation of R has passed <1 was obtained 9 (A) and 12 (B) days after control measures were implemented.

Figure 2. Average expectation of the temporal pattern of Rt after implementation of control measures according to the day T of the last observation. A) Completely effective control measures. B) Limited control measures. Simulation values of R are also given: before day 20, R = 3; after day 20 R = 0 (A) and R = 0.7 (B). The gray zone indicates that R is <1. Information that the average expectation of R has passed <1 was obtained 9 (A) and 12 (B) days after control measures were implemented.

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