Best practices for estimating and reporting epidemiological delay distributions of infectious diseases
Journal
PLOS Computational Biology
Journal Volume
20
Journal Issue
10
Start Page
e1012520
ISSN
1553-7358
Date Issued
2024-10-28
Author(s)
Kelly Charniga
Sang Woo Park
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
Editor(s)
Samuel V. Scarpino
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.
SDGs
Publisher
Public Library of Science (PLoS)
Type
journal article
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