摘要/Abstract
摘要： 目的 ·分析重庆市细菌性痢疾的空间流行病学特征及其与气象要素的相关性，并构建其发病率预测模型，为重庆市细菌性痢疾疫情的防控提供科学依据。方法 ·收集 2009—2016年重庆市细菌性痢疾及气象要素数据，并进行描述性流行病学分析，采用时空扫描统计量进行细菌性痢疾时空聚集性分析，运用 DCCA系数法量化细菌性痢疾发病率与气象要素的相关性，运用 Boruta算法结合粒子群优化算法（particle swarm optimization，PSO）及支持向量机回归模型（support vector machine for regression，SVR）构建细菌性痢疾发病率预测模型。结果 · ① 2009—2016年重庆市细菌性痢疾年均报告发病率为 29.394/100 000，0～ 5岁年龄组发病率（ 295.892/100 000）最高，散居儿童占比（ 50.335%）最大， 5月—10月为其季节性发病高峰；细菌性痢疾呈现显著的时空聚集性，一类聚集区主要集中在重庆市主城区，二类聚集区主要集中在重庆市东北地区； 6月— 10月为其主要的聚集时间。②与人群细菌性痢疾发病率具有很强相关性的气象要素分别为月平均气压（ ρDCCA -0.918）、月平均最高气温（ ρDCCA0.875）及月平均气温（ ρDCCA0.870）。③基于气象要素构建的 PSO_SVR模型均方误差（mean squared error，MSE）、平均绝对百分比误差（mean absolute percentage error，MAPE）、平方相关系数（square correlation coefficient，R2）分别为 0.055、0.101及 0.909。结论 ·重庆市主城区及渝东北地区应作为细菌性痢疾的重点防控区域，同时相关卫生部门应结合气象要素与细菌性痢疾发病率的密切相关性及其季节性高发特点，对 0～ 5岁儿童、散居儿童、农民等人群采取针对性的应对措施以控制细菌性痢疾传播与流行。基于气象要素建立的 PSO_SVR模型预测性能良好，可为细菌性痢疾的防控提供有力的理论支撑。
关键词: 细菌性痢疾, 空间流行病学, DCCA系数法, Boruta算法, 粒子群优化算法, 支持向量机回归模型
Abstract:
Objective · To analyze the spatial epidemiological characteristics of bacillary dysentery and its correlation with meteorological elements in Chongqing, and to construct its incidence prediction model, thus providing scientific basis for the prevention and control of bacterial dysentery. Methods · The data of bacterial dysentery cases and meteorological factors 2009 to 2016 in Chongqing was collected in this study. Descriptive methods were employed to investigate the epidemiological distribution of bacillary dysentery. Spatiotemporal scanning statistics was used to analyze spatiotemporal characteristics of bacillary dysentery. DCCA coefficient method was used to quantify the correlation between the incidence of bacillary dysentery and meteorological elements. Both Boruta algorithm and particle swarm optimization algorithm (PSO) combined with support vector machine for regression model (SVR) were used to establish the prediction model for the incidence of bacterial dysentery. Results · ① The mean annual reported incidence of bacillary dysentery in Chongqing 2009 to 2016 was 29.394/100 000. Children <5 years old had the highest incidence (295.892/100 000) among all age categories and scattered children had the highest proportion (50.335%) among all occupation categories. The seasonal incidence peak was May to October. Bacterial dysentery showed a significant spatial-temporal aggregation that the most likely clusters for disease was found mainly in the main urban areas and main gathering time was June to October. ② The most important meteorological elements associated with the incidence of bacterial dysentery were monthly mean atmospheric pressure (ρDCCA -0.918), monthly mean maximum temperature (ρDCCA0.875) and monthly mean temperature (ρDCCA0.870). ③ The mean squared error (MSE), mean absolute percentage error (MAPE) and square correlation coefficient (R2) of PSO_SVR model constructed based on meteorological elements were 0.055, 0.101 and 0.909, respectively. Conclusion · The main urban areas of Chongqing and the northeast of Chongqing should be regarded as the key areas for the prevention and control of bacillary dysentery. At the same time, according to the characteristics of bacillary dysentery, relevant health departments should take targeted measures to control the spread and prevalence of bacillary dysentery among children <5 years old, scattered children and farmers. The PSO_SVR model constructed based on meteorological elements has good predictive performance and can provide scientific theoretical support for the prevention and control of bacterial dysentery.
Key words: bacterial dysentery, spatial epidemiology, DCCA coefficient method, Boruta algorithm, particle swarm optimization algorithm, support vector machine regression
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