Automated Detection for Probable Homologous Foodborne Disease Outbreaks

Xiao Xiao, Yong Ge, Yunchang Guo, Danhuai Guo, Yi Shen, Yuanchun Zhou, Jianhui Li

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.