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Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines

Author

Listed:
  • Simon Pollett
  • Michael A Johansson
  • Nicholas G Reich
  • David Brett-Major
  • Sara Y Del Valle
  • Srinivasan Venkatramanan
  • Rachel Lowe
  • Travis Porco
  • Irina Maljkovic Berry
  • Alina Deshpande
  • Moritz U G Kraemer
  • David L Blazes
  • Wirichada Pan-ngum
  • Alessandro Vespigiani
  • Suzanne E Mate
  • Sheetal P Silal
  • Sasikiran Kandula
  • Rachel Sippy
  • Talia M Quandelacy
  • Jeffrey J Morgan
  • Jacob Ball
  • Lindsay C Morton
  • Benjamin M Althouse
  • Julie Pavlin
  • Wilbert van Panhuis
  • Steven Riley
  • Matthew Biggerstaff
  • Cecile Viboud
  • Oliver Brady
  • Caitlin Rivers

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. Simon Pollett and co-workers describe EPIFORGE, a guideline for reporting research on epidemic forecasting.

Suggested Citation

  • Simon Pollett & Michael A Johansson & Nicholas G Reich & David Brett-Major & Sara Y Del Valle & Srinivasan Venkatramanan & Rachel Lowe & Travis Porco & Irina Maljkovic Berry & Alina Deshpande & Moritz, 2021. "Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines," PLOS Medicine, Public Library of Science, vol. 18(10), pages 1-12, October.
  • Handle: RePEc:plo:pmed00:1003793
    DOI: 10.1371/journal.pmed.1003793
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    References listed on IDEAS

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    1. Pei-Ying Kobres & Jean-Paul Chretien & Michael A Johansson & Jeffrey J Morgan & Pai-Yei Whung & Harshini Mukundan & Sara Y Del Valle & Brett M Forshey & Talia M Quandelacy & Matthew Biggerstaff & Ceci, 2019. "A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 13(10), pages 1-21, October.
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    1. Ray, Evan L. & Brooks, Logan C. & Bien, Jacob & Biggerstaff, Matthew & Bosse, Nikos I. & Bracher, Johannes & Cramer, Estee Y. & Funk, Sebastian & Gerding, Aaron & Johansson, Michael A. & Rumack, Aaron, 2023. "Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1366-1383.

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