Social Media based Simulation Models for Understanding Disease Dynamics
Social Media based Simulation Models for Understanding Disease Dynamics
Ting Hua, Chandan K Reddy, Lei Zhang, Lijing Wang, Liang Zhao, Chang-Tien Lu, Naren Ramakrishnan
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3797-3804.
https://doi.org/10.24963/ijcai.2018/528
In this modern era, infectious diseases, such as H1N1, SARS, and Ebola, are spreading much faster than any time in history. Efficient approaches are therefore desired to monitor and track the diffusion of these deadly epidemics. Traditional computational epidemiology models are able to capture the disease spreading trends through contact network, however, one unable to provide timely updates via real-world data. In contrast, techniques focusing on emerging social media platforms can collect and monitor real-time disease data, but do not provide an understanding of the underlying dynamics of ailment propagation.
To achieve efficient and accurate real-time disease prediction, the framework proposed in this paper combines the strength of social media mining and computational epidemiology. Specifically, individual health status is first learned from user's online posts through Bayesian inference, disease parameters are then extracted for the computational models at population-level, and the outputs of computational epidemiology model are inversely fed into social media data based models for further performance improvement. In various experiments, our proposed model outperforms current disease forecasting approaches with better accuracy and more stability.
Keywords:
Machine Learning: Data Mining
Multidisciplinary Topics and Applications: Biology and Medicine
Machine Learning: Probabilistic Machine Learning
Agent-based and Multi-agent Systems: Computational Social Choice