基于Hopfield神经网络模型识别珠海市麻疹点状暴发高风险地区

目的建立Hopfield神经网络模型,对珠海市麻疹点状暴发的发病风险进行综合评估,识别高风险地区.方法 确定发病率,接种率,监测系统运转质量,疫点处置共四大类9项指标,利用矩阵实验室(Matrix Laboratory,Matlab)软件工具箱中的Hopfield神经网络模型进行建模.结果 香洲区麻疹点状暴发疫情风险等级为"极高风险",金湾区为"高风险",斗门区为"低风险".结论 Hopfield神经网络模型可对麻疹疫情风险进行综合评估,初步识别点状暴发的高风险地区.

Identification of high-risk areas for sporadic measles outbreaks based on Hopfield neural network model in Zhuhai

Objective Hopfield neural network model was established to assessment the risk of sporadic measles outbreaks and identify high risk areas. Methods 9 indicators were determined as parameters to model the Hopfield neural network using Matrix lab software. The indicators included four types of the incidence, vaccination rate, running quality of measles monitoring system, and disposition of epidemic spot. Results The level of the risk of sporadic measles outbreaks in Xiangzhou District was identified as "extreme high risk". The risk level of Jinwan District, Doumen District was "high risk"and"low risk",respectively. Conclusions Hopfield neural network model could be used to assessment the risk of sporadic measles outbreaks and identify high risk areas preliminarily.