Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action
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 researcher...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-12-01
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| Series: | Epidemics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S175543652030027X |
| _version_ | 1818599471914680320 |
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| author | Simon Pollett Michael Johansson Matthew Biggerstaff Lindsay C. Morton Sara L. Bazaco David M. Brett Major Anna M. Stewart-Ibarra Julie A. Pavlin Suzanne Mate Rachel Sippy Laurie J. Hartman Nicholas G. Reich Irina Maljkovic Berry Jean-Paul Chretien Benjamin M. Althouse Diane Myer Cecile Viboud Caitlin Rivers |
| author_facet | Simon Pollett Michael Johansson Matthew Biggerstaff Lindsay C. Morton Sara L. Bazaco David M. Brett Major Anna M. Stewart-Ibarra Julie A. Pavlin Suzanne Mate Rachel Sippy Laurie J. Hartman Nicholas G. Reich Irina Maljkovic Berry Jean-Paul Chretien Benjamin M. Althouse Diane Myer Cecile Viboud Caitlin Rivers |
| author_sort | Simon Pollett |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T12:20:08Z |
| format | Article |
| id | doaj.art-06cb538dd78a491083da1c46e070163f |
| institution | Directory Open Access Journal |
| issn | 1755-4365 |
| language | English |
| last_indexed | 2024-12-16T12:20:08Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj.art-06cb538dd78a491083da1c46e070163f2022-12-21T22:32:00ZengElsevierEpidemics1755-43652020-12-0133100400Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for actionSimon Pollett0Michael Johansson1Matthew Biggerstaff2Lindsay C. Morton3Sara L. Bazaco4David M. Brett Major5Anna M. Stewart-Ibarra6Julie A. Pavlin7Suzanne Mate8Rachel Sippy9Laurie J. Hartman10Nicholas G. Reich11Irina Maljkovic Berry12Jean-Paul Chretien13Benjamin M. Althouse14Diane Myer15Cecile Viboud16Caitlin Rivers17Viral Diseases Branch, Walter Reed Army Institute of Research, MD, USA; Corresponding author at: Johns Hopkins Center for Health Security, MD, USA.Division of Vector-Borne Diseases, Centers for Disease Control & Prevention, San Juan, Puerto Rico, USAInfluenza Division, Centers for Disease Control & Prevention, GA, USAGlobal Emerging Infections Surveillance, Armed Forces Health Surveillance Division, Silver Spring, MD, USA; Cherokee Nation Strategic Programs, Tulsa, OK, USA; Milken Institute School of Public Health, The George Washington University, Washington, DC, USAGlobal Emerging Infections Surveillance, Armed Forces Health Surveillance Division, Silver Spring, MD, USA; General Dynamics Information Technology, Falls Church, VA, USACollege of Public Health, University of Nebraska Medical Center, Omaha, NEInstitute for Global Health and Translational Science, State University of New York Upstate Medical University, Syracuse, NY, USA; InterAmerican Institute for Global Change Research (IAI), Montevideo, Department of Montevideo, UruguayNational Academies of Sciences, Engineering, and Medicine, DC, USAEmerging Infectious Diseases Branch, Walter Reed Army Institute of Research, MD, USAInstitute for Global Health and Translational Science, State University of New York Upstate Medical University, Syracuse, NY, USAGlobal Emerging Infections Surveillance, Armed Forces Health Surveillance Division, Silver Spring, MD, USA; Cherokee Nation Strategic Programs, Tulsa, OK, USAUniversity of Massachusetts at Amherst, MA, USAViral Diseases Branch, Walter Reed Army Institute of Research, MD, USADepartment of Defense, MD, USAUniversity of Washington, WA, USA; Institute for Disease Modeling, Bellevue, WA, USA; New Mexico State University, Las Cruces, NM, USAJohns Hopkins Center for Health Security, MD, USAFogarty International Center, National Institutes of Health, MD, USAJohns Hopkins Center for Health Security, MD, USAIntroduction: 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.http://www.sciencedirect.com/science/article/pii/S175543652030027XEpidemicOutbreakForecastingPredictionModelingReporting guidelines |
| spellingShingle | Simon Pollett Michael Johansson Matthew Biggerstaff Lindsay C. Morton Sara L. Bazaco David M. Brett Major Anna M. Stewart-Ibarra Julie A. Pavlin Suzanne Mate Rachel Sippy Laurie J. Hartman Nicholas G. Reich Irina Maljkovic Berry Jean-Paul Chretien Benjamin M. Althouse Diane Myer Cecile Viboud Caitlin Rivers Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action Epidemics Epidemic Outbreak Forecasting Prediction Modeling Reporting guidelines |
| title | Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action |
| title_full | Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action |
| title_fullStr | Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action |
| title_full_unstemmed | Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action |
| title_short | Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action |
| title_sort | identification and evaluation of epidemic prediction and forecasting reporting guidelines a systematic review and a call for action |
| topic | Epidemic Outbreak Forecasting Prediction Modeling Reporting guidelines |
| url | http://www.sciencedirect.com/science/article/pii/S175543652030027X |
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