Predicting epidemic tendency through search behavior analysis

Authors:
Danqing Xu;Yiqun Liu;Min Zhang;Shaoping Ma;Anqi Cui;Liyun Ru
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Year:
2011

Citing 4
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Abstract

The possibility that influenza activity can be generally detected through search log analysis has been explored in recent years. However, previous studies have mainly focused on influenza, and little attention has been paid to other epidemics. With an analysis of web user behavior data, we consider the problem of predicting the tendency of hand-foot -and-mouth disease (HFMD), whose outbreak in 2010 resulted in a great panic in China. In addition to search queries, we consider users' interactions with search engines. Given the collected search logs, we cluster HFMD-related search queries, medical pages and news reports into the following sets: epidemic-related queries (ERQs), epidemic-related pages (ERPs) and epidemic-related news (ERNs). Furthermore, we count their own frequencies as different features, and we conduct a regression analysis with current HFMD occurrences. The experimental results show that these features exhibit good performances on both accuracy and time-lines.