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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 1

Application of real-time estimation to the severe acute respiratory syndrome outbreak in Hong Kong. A) Data. B–F) Expectation (solid lines) and 95% credible intervals (dashed lines) of the real-time estimator of Rt were calculated at the end of the epidemic (B) and after a lag of 2 (C), 5 (D), 10 (E), and 20 (F) days. The gray zones indicate that R is <1.

Figure 1. Application of real-time estimation to the severe acute respiratory syndrome outbreak in Hong Kong. A) Data. B–F) Expectation (solid lines) and 95% credible intervals (dashed lines) of the real-time estimator of Rt were calculated at the end of the epidemic (B) and after a lag of 2 (C), 5 (D), 10 (E), and 20 (F) days. The gray zones indicate that R is <1.

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