{"type":"ScholarlyArticle","title":"Social Media based Simulation Models for Understanding Disease Dynamics","authors":[{"name":"Ting Hua","slug":"ting-hua","given":"Ting","family":"Hua","affiliation":"Virginia Tech , Department of Computer Science"},{"name":"Chandan K Reddy","slug":"chandan-k-reddy","given":"Chandan K.","family":"Reddy","affiliation":"Virginia Tech , Department of Computer Science"},{"name":"Lei Zhang","slug":"lei-zhang","given":"Lei","family":"Zhang","affiliation":"Virginia Tech , Department of Computer Science"},{"name":"Lijing Wang","slug":"lijing-wang","given":"Lijing","family":"Wang","affiliation":"Virginia Tech , Department of Computer Science"},{"name":"Liang Zhao","slug":"liang-zhao","given":"Liang","family":"Zhao","affiliation":"George Mason University , Department of Information Science and Technology"},{"name":"Chang-Tien Lu","slug":"chang-tien-lu","given":"Chang-Tien","family":"Lu","affiliation":"Virginia Tech , Department of Computer Science"},{"name":"Naren Ramakrishnan","slug":"naren-ramakrishnan","given":"Naren","family":"Ramakrishnan","affiliation":"Virginia Tech , Department of Computer Science"}],"abstract":"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. \r\nTo 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.","doi":"10.24963/ijcai.2018/528","url":"https://ijcai.org/proceedings/2018/528","pdf":"https://ijcai.org/proceedings/2018/0528.pdf","markdown":"https://ijcai.org/proceedings/2018/528.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/528","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":528,"pages":{"first":3797,"last":3804},"keywords":["Agent-based and Multi-agent Systems: Computational Social Choice","Machine Learning: Probabilistic Machine Learning","Multidisciplinary Topics and Applications: Biology and Medicine","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Achrekar et al., 2011] Harshavardhan Achrekar, Avinash Gandhe, Ross Lazarus, Ssu-Hsin Yu, and Benyuan Liu. 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