深圳市公共场所集中空调冷却塔水嗜肺军团菌污染预警模型研究

发布时间：2018-01-19 11:15

本文关键词： 冷却水嗜肺军团菌贝叶斯网络预警　出处：《现代预防医学》2015年10期 　论文类型：期刊论文

【摘要】：目的构建深圳市公共场所集中空调冷却塔水嗜肺军团菌污染预警模型,为预防控制军团菌病提供科学依据。方法于2013年随机选择深圳市50家安装有集中空调的公共场所进行冷却塔卫生学调查,并进行现场相关指标检测,同时采集50份冷却水水样进行微生物指标、理化指标和嗜肺军团菌检测。结合文献报道及专家咨询利用卫生学调查、现场检测和实验室检测结果构建贝叶斯网络预警模型。于2014年随机选择30家公共场所集中空调冷却塔,并收集相关数据,利用2014年收集数据对预警模型进行验证和评价。结果本研究构建的贝叶斯网络预警模型显示,对嗜肺军团菌最具有影响的因素有浊度、溶解性总固体、日照强度和防腐剂投放。该模型ROC曲线诊断临界点为0.338,利用该临界点进行嗜肺军团菌阳性诊断的灵敏度为100.0%,特异度为72.7%。结论本研究所得的贝叶斯预警模型预测的灵敏度和特异度符合风险预警的要求,可以快速判断出冷却水嗜肺军团菌污染情况。
[Abstract]:Objective to establish an early warning model of Legionella pneumophila contamination in the cooling tower of central air conditioning system in Shenzhen. Methods in 2013, 50 public places with central air conditioning in Shenzhen were randomly selected for investigation of cooling tower hygiene, and related indexes were detected. At the same time, 50 samples of cooling water were collected for microbial index, physicochemical index and pneumophilic Legionella detection. In 2014, 30 central air conditioning cooling towers in public places were randomly selected, and relevant data were collected. The data collected in 2014 were used to verify and evaluate the early warning model. Results the Bayesian network early warning model constructed in this study showed that the most influential factors for Legionella pneumophila were turbidity and total solids solubility. The diagnostic critical point of ROC curve of the model was 0.338, and the sensitivity of positive diagnosis of Legionella pneumophila by using the critical point was 100.0%. Conclusion the prediction sensitivity and specificity of the Bayesian early warning model are in line with the requirement of risk early warning, and the contamination of Legionella pneumophila in cooling water can be determined quickly.
【作者单位】：深圳市南山区疾病预防控制中心;
【基金】：深圳市南山区科技创新局资助(2012060)
【分类号】：R126.4
【正文快照】： 集中空调冷却塔由于其本身结构特点,在运行过程中有传播病原微生物的隐患,冷却塔水为军团菌的生长提供了良好的生存环境。集中空调冷却塔一直以来是世界各国最为关注的军团菌污染源之一,多次军团菌病暴发最终溯源到冷却塔[1-4]。我国1982年在南京首次发现军团菌病病例以来,已

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