Work

Real-time Estimates in Early Detection of SARS

Year: 2006

Type: article

Abstract: 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.

Source: Emerging infectious diseases

DOI: 10.3201/eid1201.050593

Authors: Simon Cauchemez, Pierre-Yves Boëlle, Christl A. Donnelly, Neil M Ferguson, Guy Thomas, Gabriel M. Leung, Anthony J Hedley, Roy M. Anderson, Alain-Jacques Valleron

Institutions: Inserm, Université Pierre-et-Marie-Curie, Assistance Publique – Hôpitaux de Paris, University of Oxford, Imperial College London, Medical Research Council, University of Hong Kong

Language: English

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W2122396923

FWCI: 9.989

Cites: 4

Cited by: 169

Related to: 10