胡茂红, 吴景文, 刘熙明. 运用数学模型探讨南昌市霍乱 与气象因素间关系[J]. 疾病监测, 2010, 25(6): 480-484. DOI: 10.3784/j.issn.1003-9961.2010.06.018
引用本文: 胡茂红, 吴景文, 刘熙明. 运用数学模型探讨南昌市霍乱 与气象因素间关系[J]. 疾病监测, 2010, 25(6): 480-484. DOI: 10.3784/j.issn.1003-9961.2010.06.018
HU Mao-hong, WU Jing-wen, LIU Xi-ming. Mathematical modeling on relationship between cholera and meteorological factors in Nanchang[J]. Disease Surveillance, 2010, 25(6): 480-484. DOI: 10.3784/j.issn.1003-9961.2010.06.018
Citation: HU Mao-hong, WU Jing-wen, LIU Xi-ming. Mathematical modeling on relationship between cholera and meteorological factors in Nanchang[J]. Disease Surveillance, 2010, 25(6): 480-484. DOI: 10.3784/j.issn.1003-9961.2010.06.018

运用数学模型探讨南昌市霍乱 与气象因素间关系

Mathematical modeling on relationship between cholera and meteorological factors in Nanchang

  • 摘要: 目的 分析江西省南昌市1998 - 2007年霍乱发生与流行与气象因素的关系,利用气象因素建立数学模型预测霍乱发病情况。 方法 收集1998 - 2007年南昌市霍乱月平均发病数与气象因素(包括月平均气温、气压、相对湿度、降雨量、风速、日照时数)数据,应用CurveExpert 1.3和SPSS 11.5软件进行霍乱发病与气象因素模型拟合和分析,根据相关参数对模型加以选择。 结果 霍乱月平均发病数分别与月平均气温、月平均日照时数呈正相关,与月平均气压呈负相关;月平均气温与霍乱月平均发病数曲线拟合良好,可应用Gunary model对霍乱发病进行预测;但是在排除共线性和混杂因素后,直线回归模型显示月平均温度仅能解释霍乱发病的37.4%。 结论 从Gunary model模型的拟合情况和月平均温度对霍乱月平均发病的解释度来分析,一方面Gunary model模型实时分析温度与霍乱发病情况,提示霍乱发生的可能性;另一方面模型预测结果要根据实际情况进行分析和判断。

     

    Abstract: Objective To analyze the relationship between cholera epidemic and meteorological factors in Nanchang from 1998 to 2007, and predict the incidence of cholera by mathematical model based on meteorological factors. Methods The average monthly incidence of cholera and meteorological factors, including average temperature, atmosphere pressure, relative humidity, precipitation, wind speed and hours with sunshine, were collected, and the Curveexpert1.3 and SPPS 11.5 were used for the fitting and analyzing the model. The best model was chosen according to relevant parameters. Results The average monthly incidence of cholera showed positive correlation with the average temperature and sunshine hours and negative correlation with the average atmosphere pressure. The curve was well-fitting between the average monthly incidence of cholera and the average temperature. The Gunary model could be used to predict the incidence of cholera, however, the linear regression model could explain only about 37.4% of cholera incidence after ruling out collinearity and confounding factors. Conclusion The well fitting of the model and the explaining degree on the relationship between temperature and the incidence of cholera indicated that the model could be used to analyze the relationship between cholera incidence and temperature in real-time and suggest the possibility of the outbreak; but the modeling result should be analyzed according the actual situation to make judgement.

     

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