Best practices for estimating and reporting epidemiological delay distributions of infectious diseases
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.
Authors
- Simon Cauchemez (ORCID: https://orcid.org/0000-0001-9186-4549)
- Natalie M. Linton (ORCID: https://orcid.org/0000-0002-5464-0076)
- Kelly Charniga (ORCID: https://orcid.org/0000-0002-7648-7041)
- Thomas Ward (ORCID: https://orcid.org/0000-0001-8801-747X)
- Juliet R.C. Pulliam (ORCID: https://orcid.org/0000-0003-3314-8223)
- Katelyn M. Gostic (ORCID: https://orcid.org/0000-0002-9369-6371)
- Anne Cori
- Sebastian Funk (ORCID: https://orcid.org/0000-0002-2842-3406)
- Christopher E. Overton (ORCID: https://orcid.org/0000-0002-8433-4010)
- Andrei R. Akhmetzhanov (ORCID: https://orcid.org/0000-0003-3269-7351)
- Jonathan Dushoff (ORCID: https://orcid.org/0000-0003-0506-4794)
- Adrian Lison (ORCID: https://orcid.org/0000-0002-6822-8437)
- Sam Abbott (ORCID: https://orcid.org/0000-0001-8057-8037)
- Sang Woo Park (ORCID: https://orcid.org/0000-0003-2202-3361)
Institutions
- Centre National de la Recherche Scientifique (FR)
- University of Liverpool (GB)
- Institut Pasteur (FR)
- National Taiwan University (TW)
- Princeton University (US)
- Université Paris Cité (FR)
- Hokkaido University (JP)
- University of Manchester (GB)
- Public Health England (GB)
- ETH Zurich (CH)
- London School of Hygiene & Tropical Medicine (GB)
- The Centers (US)
- Imperial College London (GB)
- McMaster University (CA)
Publication Details
- Journal
- PLoS Computational Biology
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1371/journal.pcbi.1012520
- Citations
- 21
- Primary Topic
- COVID-19 epidemiological studies
- Type
- article
- Field-Weighted Citation Impact
- 8.30
Funders
- London School of Hygiene and Tropical Medicine
- National Institute for Health Research Health Protection Research Unit
- National Institute for Health and Care Research
- Imperial College London
- Agence Nationale de la Recherche
- Medical Research Council
- European and Developing Countries Clinical Trials Partnership