Real-time estimates in early detection of SARS.
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.
Institut National de la Santé et de la Recherche Médicale, Paris, France. s.cauchemez@imperial.ac.uk
Simon
S
Cauchemez
Pierre-Yves
PY
Boelle
Christi A
CA
Donnelly
Neil M
NM
Ferguson
Guy
G
Thomas
Gabriel M
GM
Leung
Anthony J
AJ
Hedley
Roy M
RM
Anderson
Alain-Jacques
AJ
Valleron
D001499:Bayes Theorem; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control / Q000706:statistics & numerical data; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D045169:Severe Acute Respiratory Syndrome / Q000175:diagnosis* / Q000453:epidemiology* / Q000517:prevention & control; D013997:Time Factors