Identification and Evaluation of Epidemic Prediction and Forecasting Reporting Guidelines: A Systematic Review and a Call for Action

Authors

Simon Pollett, Walter Reed Army Institute of Research
Michael Johansson, Centers for Disease Control & Prevention
Matthew Biggerstaff, Centers for Disease Control & Prevention
Lindsay C. Morton, Armed Forces Health Surveillance Division
Sara L. Bazaco, Armed Forces Health Surveillance Division
David Brett-Major, University of Nebraska Medical CenterFollow
Anna M. Stewart-Ibarra, State University of New York Upstate Medical University
Julie A. Pavlin, National Academies of Sciences, Engineering, and Medicine
Suzanne Mate, Walter Reed Army Institute of Research
Rachel Sippy, State University of New York Upstate Medical University
Laurie J. Hartman, Armed Forces Health Surveillance Division
Nicholas G. Reich, University of Massachusetts at Amherst
Irina Maljkovic Berry, Walter Reed Army Institute of Research
Jean-Paul Chretien, Department of Defense
Benjamin M. Althouse, University of Washington
Diane Myer, Johns Hopkins Center for Health Security
Cecile Viboud, National Institutes of Health
Caitlin Rivers, Johns Hopkins Center for Health Security

Document Type

Article

Journal Title

Epidemics

Publication Date

2020

Volume

33

Abstract

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.

DOI Link

10.1016/j.epidem.2020.100400

ISSN

1878-0067

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Recommended Citation

Pollett, Simon; Johansson, Michael; Biggerstaff, Matthew; Morton, Lindsay C.; Bazaco, Sara L.; Brett-Major, David; Stewart-Ibarra, Anna M.; Pavlin, Julie A.; Mate, Suzanne; Sippy, Rachel; Hartman, Laurie J.; Reich, Nicholas G.; Maljkovic Berry, Irina; Chretien, Jean-Paul; Althouse, Benjamin M.; Myer, Diane; Viboud, Cecile; and Rivers, Caitlin, "Identification and Evaluation of Epidemic Prediction and Forecasting Reporting Guidelines: A Systematic Review and a Call for Action" (2020). Journal Articles: Epidemiology. 49.
https://digitalcommons.unmc.edu/coph_epidem_articles/49