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Online flu epidemiological deep modeling on disease contact network
GeoInformatica ( IF 2.2 ) Pub Date : 2019-07-25 , DOI: 10.1007/s10707-019-00376-9
Liang Zhao , Jiangzhuo Chen , Feng Chen , Fang Jin , Wei Wang , Chang-Tien Lu , Naren Ramakrishnan

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.

中文翻译:

疾病接触网络在线流感流行病学深度建模

诸如流感和埃博拉病毒等传染病流行的监视和预防是重要且具有挑战性的问题。因此,至关重要的是要有效,准确地表征疾病的进展和流行过程。计算流行病学可以对疾病及其潜在的联系网络的进展进行建模,但目前尚缺乏处理实时和细粒度监视数据的能力。另一方面,社交媒体提供了及时而详细的疾病监测,但对潜在的联系网络和疾病模型不敏感。为了同时应对这些挑战,本文提出了一种新颖的半监督神经网络框架,该框架融合了计算流行病学和社交媒体挖掘技术在流感流行病学建模中的优势。特别,该框架可实时学习社交媒体用户的健康状况和干预措施,并通过基础疾病模型和联系网络对其进行调整。然后,可以将从社交媒体中学到的知识输入到流行病学计算模型中,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,该算法可迭代处理上述交互式学习过程。提供的大量实验结果表明,我们的方法不仅可以在预测疾病暴发方面大幅度超越竞争方法,而且可以有效,高效地表征个体水平的疾病进展和扩散。通过基础疾病模型和联系网络进行规范。然后,可以将从社交媒体中学到的知识输入到流行病学计算模型中,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,该算法可以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,我们的方法不仅可以在预测疾病暴发方面大幅度超越竞争方法,而且可以有效,高效地表征个体水平的疾病进展和扩散。通过基础疾病模型和联系网络进行规范。然后,可以将从社交媒体中学到的知识输入到流行病学计算模型中,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,该算法可以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,我们的方法不仅可以在预测疾病暴发方面大幅度超越竞争方法,而且可以有效,高效地表征个体水平的疾病进展和扩散。我们提出了一种在线优化算法,该算法可以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,我们的方法不仅可以在预测疾病暴发方面大幅度超越竞争方法,而且可以有效,高效地表征个体水平的疾病进展和扩散。我们提出了一种在线优化算法,该算法可以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,我们的方法不仅可以在预测疾病暴发方面大幅度超越竞争方法,而且可以有效,高效地表征个体水平的疾病进展和扩散。
更新日期:2019-07-25
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