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A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern

INTRODUCTION: Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016–2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionab...

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Autores principales: Kobres, Pei-Ying, Chretien, Jean-Paul, Johansson, Michael A., Morgan, Jeffrey J., Whung, Pai-Yei, Mukundan, Harshini, Del Valle, Sara Y., Forshey, Brett M., Quandelacy, Talia M., Biggerstaff, Matthew, Viboud, Cecile, Pollett, Simon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805005/
https://www.ncbi.nlm.nih.gov/pubmed/31584946
http://dx.doi.org/10.1371/journal.pntd.0007451
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author Kobres, Pei-Ying
Chretien, Jean-Paul
Johansson, Michael A.
Morgan, Jeffrey J.
Whung, Pai-Yei
Mukundan, Harshini
Del Valle, Sara Y.
Forshey, Brett M.
Quandelacy, Talia M.
Biggerstaff, Matthew
Viboud, Cecile
Pollett, Simon
author_facet Kobres, Pei-Ying
Chretien, Jean-Paul
Johansson, Michael A.
Morgan, Jeffrey J.
Whung, Pai-Yei
Mukundan, Harshini
Del Valle, Sara Y.
Forshey, Brett M.
Quandelacy, Talia M.
Biggerstaff, Matthew
Viboud, Cecile
Pollett, Simon
author_sort Kobres, Pei-Ying
collection PubMed
description INTRODUCTION: Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016–2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionable the information produced by these studies was. METHODS: To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE, and grey literature review, we identified studies that forecasted, predicted, or simulated ecological or epidemiological phenomena related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility, and clarity by independent reviewers. RESULTS: 2034 studies were identified, of which n = 73 met the eligibility criteria. Spatial spread, R(0) (basic reproductive number), and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%), and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%), and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions, and 54% provided sufficient methodological detail to allow complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median of 119 days sooner than journal publication dates, they were used in only 30% of studies. CONCLUSIONS: Many ZIKV predictions were published during the 2016–2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics, and pandemics.
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spelling pubmed-68050052019-11-02 A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern Kobres, Pei-Ying Chretien, Jean-Paul Johansson, Michael A. Morgan, Jeffrey J. Whung, Pai-Yei Mukundan, Harshini Del Valle, Sara Y. Forshey, Brett M. Quandelacy, Talia M. Biggerstaff, Matthew Viboud, Cecile Pollett, Simon PLoS Negl Trop Dis Research Article INTRODUCTION: Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016–2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionable the information produced by these studies was. METHODS: To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE, and grey literature review, we identified studies that forecasted, predicted, or simulated ecological or epidemiological phenomena related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility, and clarity by independent reviewers. RESULTS: 2034 studies were identified, of which n = 73 met the eligibility criteria. Spatial spread, R(0) (basic reproductive number), and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%), and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%), and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions, and 54% provided sufficient methodological detail to allow complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median of 119 days sooner than journal publication dates, they were used in only 30% of studies. CONCLUSIONS: Many ZIKV predictions were published during the 2016–2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics, and pandemics. Public Library of Science 2019-10-04 /pmc/articles/PMC6805005/ /pubmed/31584946 http://dx.doi.org/10.1371/journal.pntd.0007451 Text en https://creativecommons.org/publicdomain/zero/1.0/ 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 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Kobres, Pei-Ying
Chretien, Jean-Paul
Johansson, Michael A.
Morgan, Jeffrey J.
Whung, Pai-Yei
Mukundan, Harshini
Del Valle, Sara Y.
Forshey, Brett M.
Quandelacy, Talia M.
Biggerstaff, Matthew
Viboud, Cecile
Pollett, Simon
A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern
title A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern
title_full A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern
title_fullStr A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern
title_full_unstemmed A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern
title_short A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern
title_sort systematic review and evaluation of zika virus forecasting and prediction research during a public health emergency of international concern
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805005/
https://www.ncbi.nlm.nih.gov/pubmed/31584946
http://dx.doi.org/10.1371/journal.pntd.0007451
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