Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm.
Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm.
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DOI：
10.1186/1741-7015-10-165
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发表时间：
2012-12-13
期刊：
影响因子：
9.3
通讯作者：
Vespignani A
中科院分区：
文献类型：
--
作者：
Tizzoni M;Bajardi P;Poletto C;Ramasco JJ;Balcan D;Gonçalves B;Perra N;Colizza V;Vespignani A
传染病的数学和计算模型越来越多地用于支持公共卫生决策；然而，它们的可靠性目前仍存在争议。使用数据驱动模型对流行病传播进行实时预测受到参数估计和验证带来的技术挑战的阻碍。为 2009 年 H1N1 流感危机收集的数据为验证实时模型预测和定义不同方法的主要成功标准提供了前所未有的机会。我们使用全球流行病和流动模型对全球流行病传播进行随机模拟，以每日分辨率得出 220 个国家 3,362 个亚群的发病率和传播事件（以及其他指标）。该模型利用蒙特卡罗最大似然分析，对 H1N1 流感大流行早期阶段的季节性传播潜力进行了估计，并生成了北半球秋/冬季波活动高峰的集合预测。这些结果根据在 48 个国家收集的现实生活监测数据进行了验证，并通过重点评估其稳健性：1）大流行的高峰时间； 2）模型允许的空间分辨率水平； 3）临床发病率和疫苗的有效性。此外，我们还研究了数据不完整性对预测可靠性的影响。研究发现，峰值时间的实时预测与经验数据非常吻合，对可能无法实时获取的数据（例如暴露前免疫力和疫苗接种活动的遵守情况）显示出很强的鲁棒性，但这会影响发病率的预测。大流行的时间和空间展开对于模型中集成的流动数据的水平至关重要。我们的结果表明，大规模模型可用于提供有价值的流感传播实时预测，但它们需要高性能计算。预测的质量取决于数据集成的水平，因此强调基于人口的模型中需要高质量的数据，以及逐步更新经过验证的可用经验知识来为这些模型提供信息。
Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models.
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DOI：
10.1073/pnas.0906910106
发表时间：
2009-12-22
影响因子：
11.1
作者：
Balcan, Duygu;Colizza, Vittoria;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
3.7
作者：
Brooks-Pollock E;Tilston N;Edmunds WJ;Eames KT
通讯作者：
Eames KT
影响因子：
3.7
作者：
Bobashev G;Morris RJ;Goedecke DM
通讯作者：
Goedecke DM
影响因子：
5
作者：
Chao, Dennis L.;Matrajt, Laura;Longini, Ira M., Jr.
通讯作者：
Longini, Ira M., Jr.
影响因子：
5.4
作者：
Allwinn, Regina;Geiler, Janina;Doerr, H. W.
通讯作者：
Doerr, H. W.