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We propose a Bayesian statistical framework for estimating the reproduction number R early in an epidemic. This method allows for the yet-unrecorded secondary cases if the estimate is obtained before the epidemic has ended. We applied our approach to the severe acute respiratory syndrome (SARS) epidemic that started in February 2003 in Hong Kong. Temporal patterns of R estimated after 5, 10, and 20 days were similar. Ninety-five percent credible intervals narrowed when more data were available but stabilized after 10 days. Using simulation studies of SARS-like outbreaks, we have shown that the method may be used for early monitoring of the effect of control measures.

Original publication




Journal article


Emerging infectious diseases

Publication Date





110 - 113


Institut National de la Santé et de la Recherche Médicale, Paris, France.


Humans, Severe Acute Respiratory Syndrome, Bayes Theorem, Disease Outbreaks, Time Factors, Computer Simulation, Hong Kong