@inproceedings{4282398516b1498789f2d9063548934d,
title = "Automated detection for probable homologous foodborne disease outbreaks",
abstract = "Foodborne disease, a rapid-growing public health problem, has become the highest-priority topic for food safety. The threat of foodborne disease has stimulated interest in enhancing public health surveillance to detect outbreaks rapidly. To advance research on food risk assessment in China, China National Center for Food Safety Risk Assessment (CFSA) sponsored a project to construct an online correlation analysis system for foodborne disease surveillance beginning in October 2012. They collect foodborne disease clinical data from sentinel hospitals across the country. They want to analyze the foodborne disease outbreaks existed in the collected data and finally find the link between pathogen, incriminated food sources and infected persons. Rapid detection of outbreaks is a critical first step for the analysis. The purpose of this paper is to provide approaches that can be applied to an online system to rapidly find local and sporadic foodborne disease outbreaks out of the collected data. Specifically, we employ DBSCAN for local outbreaks detection and solve the parameter self-adaptive problem in DBSCAN. We also propose a new approach named K-CPS (K-Means Clustering with Pattern Similarity) to detect sporadic outbreaks. The experimental results show that our methods are effective for rapidly mining local and sporadic outbreaks from the dataset.",
keywords = "Clustering, Foodborne disease outbreak detection, Frequent patterns, Parameters self-adaptive",
author = "Xiao Xiao and Yong Ge and Yunchang Guo and Danhuai Guo and Yi Shen and Yuanchun Zhou and Jianhui Li",
note = "Funding Information: This work is partly supported by Special Research Funding of National Health and Family Planning Commission of China under grant No.201302005, Natural Science Foundation of China under Grant No. 41371386, 91224006, the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA06010307, XDA05050601, 12th Five-Year Plan for Science & Technology Support under Grant No.2013BAD15B02. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 ; Conference date: 19-05-2015 Through 22-05-2015",
year = "2015",
doi = "10.1007/978-3-319-18038-0_44",
language = "English (US)",
isbn = "9783319180373",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "563--575",
editor = "Tu-Bao Ho and Hiroshi Motoda and Hiroshi Motoda and Ee-Peng Lim and Tru Cao and David Cheung and Zhi-Hua Zhou",
booktitle = "Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings",
}