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

• Published in: PLOS Neglected Tropical Diseases
• Main Authors: Pei-Ying Kobres, Jean-Paul Chretien, Michael A Johansson, Jeffrey J Morgan, Pai-Yei Whung, Harshini Mukundan, Sara Y Del Valle, Brett M Forshey, Talia M Quandelacy, Matthew Biggerstaff, Cecile Viboud, Simon Pollett
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2019
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic; Barré; Prisma
• Online Access: https://doi.org/10.1371/journal.pntd.0007451; https://doaj.org/article/1c08ee27b7d3461ba6b4f30cad580c98

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, R0 (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 ...