School of Public Health, George Washington University, Washington, DC, United States of America.
Department of Defense, Fort Detrick, Maryland, United States of America.
PLoS Negl Trop Dis. 2019 Oct 4;13(10):e0007451. doi: 10.1371/journal.pntd.0007451. eCollection 2019 Oct.
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.
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.
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 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.
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.
流行病情报预测工具具有在突发疫情中提供可操作信息的潜力。虽然在 2016-2017 年寨卡病毒（ZIKV）大流行期间发表了许多预测性研究，但目前尚不清楚这些研究产生的信息的及时性、可重复性和可操作性如何。
为了提高数学模型在支持未来传染病爆发中的实际应用功能，我们按照 PRISMA 指南对最近的寨卡病毒大流行期间发表的所有寨卡病毒预测研究进行了系统回顾。通过 MEDLINE、EMBASE 和灰色文献综述，我们确定了截至 2017 年 3 月 1 日发表的预测、预测或模拟与寨卡大流行相关的生态或流行病学现象的研究。符合条件的研究由独立评审员评估目标、数据源、方法、及时性、可重复性、可及性和清晰度。
共确定了 2034 项研究，其中 n = 73 项符合纳入标准。空间传播、R0（基本繁殖数）和流行动态最常被预测，而预测发病率较低 （4%）、性传播风险（4%）和干预影响（4%）。大多数研究专门研究了美洲地区的人群（52%），很少有针对非洲的研究（4%）。病例数（67%）、媒介（41%）和人口统计数据（37%）是最常见的数据源。实时互联网数据和病原体基因组信息分别在 7%和 0%的研究中使用，而社会科学和行为数据通常不在建模工作中。确定性模型比随机方法更受欢迎。40%的研究完全提供了模型数据，29%提供了所有相关的模型代码，43%对所有预测都呈现了不确定性，54%提供了足够的方法细节以实现完全可重复性。51%的预测是在美洲疫情高峰后发表的。虽然预印本的使用使寨卡病毒预测的可及性平均提前了 119 天，但仅在 30%的研究中使用。
在 2016-2017 年寨卡病毒大流行期间，发表了许多寨卡病毒预测。这些已发表的预测在可及性、可重复性、及时性和不确定性的纳入方面存在差异，表明仍有很大的改进空间。为了提高分析工具在疫情应对中的实用性，对于未来的疫情爆发、流行和大流行，必须改善模型数据、代码和预印本的共享。
