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

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 and Caitlin Rivers

PLOS Medicine, 2021, vol. 18, issue 10, 1-12

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pmed00:1003793

DOI: 10.1371/journal.pmed.1003793

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