Case-based Reasoning in Avian Influenza Risk Early Warning

摘要

Techniques to predict epidemic diseases and generate early warnings are widely studied in current complex environments. Case-based reasoning (CBR) has advantages of using past experience to generate warnings for new situations. This paper presents a method by using CBR to map risks of avian influenza transmission of obscure reasons. This method applies fuzzy logic into CBR and combines CBR with expert rules to improve capacities of risk predictions. An illustrative system shows how to conduct risk prediction based on previous influenza cases, poultry trade history, and migratory birds' movement information.