Risk Factor Analysis of West Nile Virus Using Structural Learning with Forgetting Method

A novel neural network based approach for risk factor analysis of infection of West Nile virus (WNV) is proposed. A multi-factor risk analysis model is developed and learnt by an algorithm called structural learning with forgetting. Through the learning, unnecessary connections fade away and a skeletal network emerges. By analyzing the resulted skeletal networks, significant risk factors can be identified, and thus a more thorough understanding of WNV transmission mechanism can be obtained. The proposed approach is tested with a dead birds surveillance data. The results demonstrate the effectiveness of the proposed approach.