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


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
Oct 2024
Historique:
medline: 28 10 2024
pubmed: 28 10 2024
entrez: 28 10 2024
Statut: epublish

Résumé

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.

Identifiants

pubmed: 39466727
doi: 10.1371/journal.pcbi.1012520
pii: PCOMPBIOL-D-24-00771
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1012520

Informations de copyright

Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Kelly Charniga (K)

Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Paris, France.

Sang Woo Park (SW)

Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.

Andrei R Akhmetzhanov (AR)

College of Public Health, National Taiwan University, Taipei, Taiwan.

Anne Cori (A)

MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.

Jonathan Dushoff (J)

Departments of Mathematics & Statistics and Biology, McMaster University, Hamilton, Ontario, Canada.
Department of Biology, McMaster University, Hamilton, Ontario, Canada.
M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada.

Sebastian Funk (S)

Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.

Katelyn M Gostic (KM)

Center for Forecasting and Outbreak Analytics, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.

Natalie M Linton (NM)

Graduate School of Medicine, Hokkaido University, Sapporo-shi, Hokkaido, Japan.

Adrian Lison (A)

Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.

Christopher E Overton (CE)

Department of Mathematical Sciences, University of Liverpool, Liverpool, United Kingdom.
All Hazards Intelligence, Infectious Disease Modelling Team, Data Analytics and Surveillance, UK Health Security Agency, United Kingdom.
Department of Mathematics, University of Manchester, Manchester, United Kingdom.

Juliet R C Pulliam (JRC)

Center for Forecasting and Outbreak Analytics, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.

Thomas Ward (T)

All Hazards Intelligence, Infectious Disease Modelling Team, Data Analytics and Surveillance, UK Health Security Agency, United Kingdom.

Simon Cauchemez (S)

Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Paris, France.

Sam Abbott (S)

Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.

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Classifications MeSH