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Best practices for estimating and reporting epidemiological delay distributions of infectious diseases

Author

Listed:
  • Kelly Charniga
  • Sang Woo Park
  • Andrei R Akhmetzhanov
  • Anne Cori
  • Jonathan Dushoff
  • Sebastian Funk
  • Katelyn M Gostic
  • Natalie M Linton
  • Adrian Lison
  • Christopher E Overton
  • Juliet R C Pulliam
  • Thomas Ward
  • Simon Cauchemez
  • Sam Abbott

Abstract

Epidemiological delays are key quantities that inform public health policy and clinical practice. They are used as inputs for mathematical and statistical models, which in turn can guide control strategies. In recent work, we found that censoring, right truncation, and dynamical bias were rarely addressed correctly when estimating delays and that these biases were large enough to have knock-on impacts across a large number of use cases. Here, we formulate a checklist of best practices for estimating and reporting epidemiological delays. We also provide a flowchart to guide practitioners based on their data. Our examples are focused on the incubation period and serial interval due to their importance in outbreak response and modeling, but our recommendations are applicable to other delays. The recommendations, which are based on the literature and our experience estimating epidemiological delay distributions during outbreak responses, can help improve the robustness and utility of reported estimates and provide guidance for the evaluation of estimates for downstream use in transmission models or other analyses.

Suggested Citation

  • Kelly Charniga & Sang Woo Park & Andrei R Akhmetzhanov & Anne Cori & Jonathan Dushoff & Sebastian Funk & Katelyn M Gostic & Natalie M Linton & Adrian Lison & Christopher E Overton & Juliet R C Pulliam, 2024. "Best practices for estimating and reporting epidemiological delay distributions of infectious diseases," PLOS Computational Biology, Public Library of Science, vol. 20(10), pages 1-21, October.
    • Handle: RePEc:plo:pcbi00:1012520
    DOI: 10.1371/journal.pcbi.1012520
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    References listed on IDEAS

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    1. Matthew Hall & Mark Woolhouse & Andrew Rambaut, 2015. "Epidemic Reconstruction in a Phylogenetics Framework: Transmission Trees as Partitions of the Node Set," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-36, December.
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