TY - JOUR
T1 - Recommended reporting items for epidemic forecasting and prediction research
T2 - The EPIFORGE 2020 guidelines
AU - Pollett, Simon
AU - Johansson, Michael A.
AU - Reich, Nicholas G.
AU - Brett-Major, David
AU - Del Valle, Sara Y.
AU - Venkatramanan, Srinivasan
AU - Lowe, Rachel
AU - Porco, Travis
AU - Berry, Irina Maljkovic
AU - Deshpande, Alina
AU - Kraemer, Moritz U.G.
AU - Blazes, David L.
AU - Pan-Ngum, Wirichada
AU - Vespigiani, Alessandro
AU - Mate, Suzanne E.
AU - Silal, Sheetal P.
AU - Kandula, Sasikiran
AU - Sippy, Rachel
AU - Quandelacy, Talia M.
AU - Morgan, Jeffrey J.
AU - Ball, Jacob
AU - Morton, Lindsay C.
AU - Althouse, Benjamin M.
AU - Pavlin, Julie
AU - van Panhuis, Wilbert
AU - Riley, Steven
AU - Biggerstaff, Matthew
AU - Viboud, Cecile
AU - Brady, Oliver
AU - Rivers, Caitlin
N1 - Publisher Copyright:
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
PY - 2021/10
Y1 - 2021/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85117437692&partnerID=8YFLogxK
U2 - 10.1371/journal.pmed.1003793
DO - 10.1371/journal.pmed.1003793
M3 - Article
C2 - 34665805
AN - SCOPUS:85117437692
SN - 1549-1277
VL - 18
JO - PLoS Medicine
JF - PLoS Medicine
IS - 10
M1 - e1003793
ER -