目的:每年的流感流行是卫生保健系统的沉重负担,并日益成为某些地区的主要公共卫生问题,如香港(中国)。因此,基于多种机器学习方法,考虑到香港的季节性流感,目的建立组合判断分类器(CJC)模型,对疫情趋势进行分类,提高流感疫情预警的准确性。
方法:采用单因素统计方法选取特征变量,建立流感暴发的影响因素体系。在此基础上,提出CJC模型是为了提供流感爆发的早期预警。最终模型中的特征变量包括大气压力,绝对最高温度,平均温度,绝对最低温度,平均露点温度,季节性流感病毒阳性检测的数量,所有呼吸道标本中的阳性百分比,以及主要诊断为流感的公立医院的入院率。
结果:CJC模型对流感爆发趋势的准确率达到96.47%,该模型的敏感性和特异性变化率低于其他模型.因此,CJC模型具有更稳定的预测性能。在本研究中,以香港近年来的疫情和气象数据为研究对象,并且发现影响因素与流感爆发之间存在滞后相关性。然而,一些潜在的风险因素,例如地理自然和人为因素,没有成立,理想情况下,这在一定程度上影响了预测性能。
结论:一般来说,CJC模型在统计上表现出更好的性能,与一些经典的预警算法相比,如支持向量机,判别分析,和合奏类,这提高了季节性流感的预警性能。
OBJECTIVE: The annual influenza epidemic is a heavy burden on the health care system, and has increasingly become a major public health problem in some areas, such as Hong Kong (China). Therefore, based on a variety of machine learning methods, and considering the seasonal influenza in Hong Kong, the study aims to establish a Combinatorial Judgment Classifier (CJC) model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.
METHODS: The characteristic variables were selected using the single-factor statistical method to establish the influencing factor system of an influenza outbreak. On this basis, the CJC model was proposed to provide an early warning for an influenza outbreak. The characteristic variables in the final model included atmospheric pressure, absolute maximum temperature, mean temperature, absolute minimum temperature, mean dew point temperature, the number of positive detections of seasonal influenza viruses, the positive percentage among all respiratory specimens, and the admission rates in public hospitals with a principal diagnosis of influenza.
RESULTS: The accuracy of the CJC model for the influenza outbreak trend reached 96.47%, the sensitivity and specificity change rates of this model were lower than those of other models. Hence, the CJC model has a more stable prediction performance. In the present study, the epidemic situation and meteorological data of Hong Kong in recent years were used as the research objects for the construction of the model index system, and a lag correlation was found between the influencing factors and influenza outbreak. However, some potential risk factors, such as geographical nature and human factors, were not incorporated, which ideally affected the prediction performance to some extent.
CONCLUSIONS: In general, the CJC model exhibits a statistically better performance, when compared to some classical early warning algorithms, such as Support Vector Machine, Discriminant Analysis, and Ensemble Classfiers, which improves the performance of the early warning of seasonal influenza.