Optimizing the precision of case fatality ratio estimates under the surveillance pyramid approach

Camille Pelat*, Neil M. Ferguson, Peter J. White, Carrie Reed, Lyn Finelli, Simon Cauchemez, Christophe Fraser

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


In the management of emerging infectious disease epidemics, precise and accurate estimation of severity indices, such as the probability of death after developing symptoms-the symptomatic case fatality ratio (sCFR)-is essential. Estimation of the sCFR may require merging data gathered through different surveillance systems and surveys. Since different surveillance strategies provide different levels of precision and accuracy, there is need for a theory to help investigators select the strategy that maximizes these properties. Here, we study the precision of sCFR estimators that combine data from several levels of the severity pyramid. We derive a formula for the standard error, which helps us find the estimator with the best precision given fixed resources. We further propose rules of thumb for guiding the choice of strategy: For example, should surveillance of a particular severity level be started? Which level should be preferred? We derive a formula for the optimal allocation of resources between chosen surveillance levels and provide a simple approximation that can be used in thinking more heuristically about planning surveillance. We illustrate these concepts with numerical examples corresponding to 3 influenza pandemic scenarios. Finally, we review the equally important issue of accuracy.

Original languageEnglish
Pages (from-to)1036-1046
Number of pages11
JournalAmerican Journal of Epidemiology
Issue number10
Publication statusPublished - 12 Sept 2014
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2014 The Author.


  • Case fatality ratio
  • Emerging infectious diseases
  • Influenza
  • Pandemics
  • Statistical planning
  • Surveillance protocol


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