A Hybrid Knowledge-based Risk Prediction Method Using Fuzzy Logic and CBR for Avian Influenza Early Warning

The threat of highly pathogenic avian influenza persists, with the size of the epidemic growing worldwide. Various methods have been applied to measure and predict the threat. This paper outlines our research which develops a knowledge-based method that makes full use of previous knowledge to perform a comprehensive forecast of the risk of avian influenza and generate reliable warning signals for a specific region at a specific time. The method contains a risk estimation model and a knowledge-based prediction method using fuzzy logic and case-based reasoning (CBR) to generate timely early warnings to support decision makers to identify underlying vulnerabilities and implement relevant strategies. An example is presented that illustrates the capabilities and procedures of the proposed method in avian influenza early warning systems.