Predicting Epidemic Tendency Through Search Behavior Analysis
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 out-break in 2010 resulted in a great panic in China. In addi-tion 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 ep-idemic-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 timeliness.
Cite
Text
Xu et al. "Predicting Epidemic Tendency Through Search Behavior Analysis." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-393Markdown
[Xu et al. "Predicting Epidemic Tendency Through Search Behavior Analysis." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/xu2011ijcai-predicting/) doi:10.5591/978-1-57735-516-8/IJCAI11-393BibTeX
@inproceedings{xu2011ijcai-predicting,
title = {{Predicting Epidemic Tendency Through Search Behavior Analysis}},
author = {Xu, Danqing and Liu, Yiqun and Zhang, Min and Ma, Shaoping and Cui, Anqi and Ru, Liyun},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {2361-2366},
doi = {10.5591/978-1-57735-516-8/IJCAI11-393},
url = {https://mlanthology.org/ijcai/2011/xu2011ijcai-predicting/}
}