Real-time Estimates in Early Detection of SARS

Details

• Alternative Title:
  Emerg Infect Dis
• Personal Author:
  Cauchemez, Simon ; Boëlle, Pierre-Yves ; Donnelly, Christl A. ; Ferguson, Neil M ; Thomas, Guy ; Leung, Gabriel M. ; Hedley, Anthony J ; Anderson, Roy M. ; Valleron, Alain-Jacques
• Description:
  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.
• Subjects:
  Bayes Theorem Computer Simulation Disease Outbreaks Humans Research Severe Acute Respiratory Syndrome Time Factors
• Source:
  Emerg Infect Dis. 12(1):110-113.
• Document Type:
  Journal Article
• Volume:
  12
• Issue:
  1
• Collection(s):
  Emerging Infectious Diseases
• Main Document Checksum:
  urn:sha256:2e65aa299ffcd03d3335a839289f8b8c6b83ba1892fb2f534216fbc29534ec40
• Download URL:
  https://stacks.cdc.gov/view/cdc/15935/cdc_15935_DS1.pdf
• File Type:
  [PDF - 167.08 KB ]