Simon Pollett, Walter Reed Army Institute of Research
Michael A. Johansson, Centers for Disease Control & Prevention
Nicholas G. Reich, University of Massachusetts Amherst
David Brett-Major, University of Nebraska Medical CenterFollow
Sara Y. Del Valle, Los Alamos National Laboratory
Srinivasan Venkatramanan, University of Virginia
Rachel Lowe, London School of Hygiene & Tropical Medicine
Travis Porco, University of California at San Francisco
Irina Maljkovic Berry, Walter Reed Army Institute of Research
Alina Deshpande, Los Alamos National Laboratory
Moritz U. G. Kraemer, University of Oxford
David L. Blazes, Bill and Melinda Gates Foundation
Wirichada Pan-Ngum, Mahidol University
Alessandro Vespigiani, Northeastern University
Suzanne E. Mate, Walter Reed Army Institute of Research
Sheetal P. Silal, University of Cape Town
Sasikiran Kandula, Columbia University
Rachel Sippy, State University of New York Upstate Medical University
Talia M. Quandelacy, Centers for Disease Control & Prevention
Jeffrey J. Morgan, Catholic University of America
Jacob Ball, U.S. Army Public Health Center
Lindsay C. Morton, Global Emerging Infections Surveillance
Benjamin M. Althouse, University of Washington
Julie Pavlin, National Academies of Sciences, Engineering, and Medicine
Wilbert van Panhuis, University of Pittsburgh Graduate School of Public Health
Steven Riley, Imperial College London
Matthew Biggerstaff, Centers for Disease Control & Prevention
Cecile Viboud, National Institutes for Health
Oliver Brady, London School of Hygiene & Tropical Medicine
Caitlin Rivers, Johns Hopkins Bloomberg School of Public Health

Document Type

Article

Journal Title

PLoS Medicine

Publication Date

2021

Volume

18

Abstract

BACKGROUND: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research.

METHODS AND FINDINGS: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies.

CONCLUSIONS: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

ISSN

1549-1676

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Public Domain Dedication 1.0 License.

Rights

U.S. Government Work

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