TY - JOUR
T1 - Identification and evaluation of epidemic prediction and forecasting reporting guidelines
T2 - A systematic review and a call for action
AU - Pollett, Simon
AU - Johansson, Michael
AU - Biggerstaff, Matthew
AU - Morton, Lindsay C.
AU - Bazaco, Sara L.
AU - Brett Major, David M.
AU - Stewart-Ibarra, Anna M.
AU - Pavlin, Julie A.
AU - Mate, Suzanne
AU - Sippy, Rachel
AU - Hartman, Laurie J.
AU - Reich, Nicholas G.
AU - Maljkovic Berry, Irina
AU - Chretien, Jean Paul
AU - Althouse, Benjamin M.
AU - Myer, Diane
AU - Viboud, Cecile
AU - Rivers, Caitlin
N1 - Publisher Copyright:
© 2020 The Authors
PY - 2020/12
Y1 - 2020/12
N2 - Introduction: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. Methods: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. Results: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. Conclusions: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.
AB - Introduction: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. Methods: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. Results: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. Conclusions: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.
KW - Epidemic
KW - Forecasting
KW - Modeling
KW - Outbreak
KW - Prediction
KW - Reporting guidelines
UR - http://www.scopus.com/inward/record.url?scp=85094604949&partnerID=8YFLogxK
U2 - 10.1016/j.epidem.2020.100400
DO - 10.1016/j.epidem.2020.100400
M3 - Article
C2 - 33130412
AN - SCOPUS:85094604949
SN - 1755-4365
VL - 33
JO - Epidemics
JF - Epidemics
M1 - 100400
ER -