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

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A new checklist, EPIFORGE, standardizes reporting for infectious disease epidemic forecasting research. This guideline aims to improve the quality and consistency of forecasting studies, crucial for public health.

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Area of Science:

  • Epidemiology
  • Public Health
  • Infectious Disease Modeling

Background:

  • Decades of infectious disease outbreaks, including COVID-19, highlight the need for robust epidemic forecasting and prediction research.
  • Existing medical research fields have reporting guidelines, but epidemic forecasting lacks standardized reporting criteria.
  • This gap hinders the consistency, reproducibility, and quality of forecasting studies.

Purpose of the Study:

  • To develop and introduce the EPIFORGE checklist, a novel guideline for the standardized reporting of epidemic forecasting and prediction research.
  • To enhance the quality, consistency, and comparability of scientific reporting in epidemic forecasting.
  • To provide a benchmark for reporting critical methodological details in infectious disease modeling studies.

Main Methods:

  • The EPIFORGE checklist was developed using a best-practice approach for guideline creation.
  • A Delphi process involving international experts in infectious disease modeling was employed.
  • Broad consultation with modelers and end-users ensured comprehensive input and relevance.

Main Results:

  • The EPIFORGE checklist provides a standardized framework for reporting epidemic forecasting research.
  • The checklist aims to improve the consistency and reproducibility of forecasting study methodologies.
  • It serves as a reporting standard, not a guide for conducting research.

Conclusions:

  • The EPIFORGE checklist has been submitted to the EQUATOR network for wider dissemination.
  • The guideline is also available on dedicated webpages to encourage feedback and journal endorsement.
  • Standardized reporting through EPIFORGE is expected to advance the field of epidemic forecasting.