Simon PollettMichael A. JohanssonNicholas G. ReichDavid Brett-MajorSara Y. Del ValleSrinivasan VenkatramananRachel LoweTravis PorcoIrina Maljkovic BerryAlina DeshpandeMoritz U.G. KraemerDavid L. BlazesWirichada Pan-NgumAlessandro VespigianiSuzanne E. MateSheetal P. SilalSasikiran KandulaRachel SippyTalia M. QuandelacyJeffrey J. MorganJacob BallLindsay C. MortonBenjamin M. AlthouseJulie PavlinWilbert van PanhuisSteven RileyMatthew BiggerstaffCecile ViboudOliver BradyCaitlin RiversInstitute for Disease ModelingInstituto de Salud Global de BarcelonaNational Center for Emerging and Zoonotic Infectious DiseasesMilken Institute School of Public HealthLondon School of Hygiene & Tropical MedicineNortheastern UniversityFogarty International Center (FIC)University of OxfordUniversity of California, San FranciscoBill and Melinda Gates FoundationUniversity of VirginiaCenters for Disease Control and PreventionCatholic University of AmericaImperial College Faculty of MedicineSUNY Upstate Medical UniversityNational Academies of Sciences, Engineering, and MedicineUniversity of Nebraska Medical CenterUniversity of WashingtonUniversity of Massachusetts AmherstWalter Reed Army Institute of ResearchMailman School of Public HealthMahidol UniversityNuffield Department of MedicineJohns Hopkins Bloomberg School of Public HealthLos Alamos National LaboratoryUniversity of Pittsburgh Graduate School of Public HealthNew Mexico State UniversityUniversity of Cape TownU.S. Army Public Health CenterArmed Forces Health Surveillance Center2022-08-042022-08-042021-10-01PLoS Medicine. Vol.18, No.10 (2021)15491676154912772-s2.0-85117437692https://repository.li.mahidol.ac.th/handle/20.500.14594/77798Background 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.Mahidol UniversityMedicineArticleSCOPUS10.1371/journal.pmed.1003793