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• [P1039] A Hybrid Knowledge-based Prediction Method for Avian Influenza Early Warning
  Author(s): Guangquan Zhang, Jie Zhang, Jie Lu
  
  

  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 fully 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 predict 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.

  
   

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