Online flu epidemiological deep modeling on disease contact network

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.