A hybrid knowledge-based prediction method for avian influenza early warning

Authors:
Jie Zhang;Jie Lu;Guangquan Zhang
Affiliations:
Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, Australia;Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, Australia;Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, Australia
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

High pathogenic avian influenza remains rampant and the epidemic size has been growing in the world. The early warning system (EWS) for avian influenza becomes increasingly essential to militating against the risk of outbreak crisis. An EWS can generate timely early warnings to support decision makers in identifying underlying vulnerabilities and implementing relevant strategies. This paper addresses this crucial issue and focuses on how to make full use of previous events to perform comprehensive forecasting and generate reliable warning signals. It proposes a hybrid knowledge-based prediction (HKBP) method which combines case-based reasoning (CBR) with the fuzzy logic technique. The method can improve the prediction accuracy for avian influenza in a specific region at a specific time. An example is presented to illustrate the capabilities and procedures of the HKBP method.