Online flu epidemiological deep modeling on disease contact network
Online flu epidemiological deep modeling on disease contact network
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DOI：
10.1007/s10707-019-00376-9
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发表时间：
2019-07
期刊：
影响因子：
2
通讯作者：
Liang Zhao;Jiangzhuo Chen;Feng Chen;Fang Jin;Wei Wang;Chang-Tien Lu;Naren Ramakrishnan
中科院分区：
文献类型：
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作者：
Liang Zhao;Jiangzhuo Chen;Feng Chen;Fang Jin;Wei Wang;Chang-Tien Lu;Naren Ramakrishnan
监测和预防流感和埃博拉等传染病疫情是重要而具有挑战性的问题。因此，有效和准确地描述疾病进展和流行过程是至关重要的。计算流行病学可以模拟疾病的发展及其潜在的接触网络，但到目前为止还缺乏处理实时和细粒度监测数据的能力。另一方面，社交媒体提供及时和详细的疾病监测，但对潜在的接触网络和疾病模型不敏感。为了同时应对这些挑战，本文提出了一种新的半监督神经网络框架，该框架集成了计算流行病学和社交媒体挖掘技术的优势，用于流感流行病学建模。具体地说，该框架实时了解社交媒体用户的健康状态和干预行动，并通过潜在的疾病模型和联系网络进行规律化。然后，可以将从社交媒体学习的知识反馈到计算流行病模型中，以提高疾病扩散建模的效率和准确性。我们提出了一种在线优化算法，该算法迭代地处理上述交互式学习过程。提供的大量实验结果表明，我们的方法不仅在预测疾病爆发方面比竞争方法有很大的优势，而且能够有效和高效地表征个体水平的疾病进展和扩散。
The surveillance and preventions of infectious disease epidemics such as influenza and Ebola are important and challenging issues. It is therefore crucial to characterize the disease progress and epidemics process efficiently and accurately. Computational epidemiology can model the progression of the disease and its underlying contact network, but as yet lacks the ability to process of real-time and fine-grained surveillance data. Social media, on the other hand, provides timely and detailed disease surveillance but is insensible to the underlying contact network and disease model. To address these challenges simultaneously, this paper proposes a novel semi-supervised neural network framework that integrates the strengths of computational epidemiology and social media mining techniques for influenza epidemiological modeling. Specifically, this framework learns social media users’ health states and intervention actions in real time, regularized by the underlying disease model and contact network. The learned knowledge from social media can then be fed into the computational epidemic model to improve the efficiency and accuracy of disease diffusion modeling. We propose an online optimization algorithm that iteratively processes the above interactive learning process. The extensive experimental results provided demonstrated that our approach can not only outperform competing methods by a substantial margin in forecasting disease outbreaks, but also characterize the individual-level disease progress and diffusion effectively and efficiently.