【摘要】 目的促进分布滞后非线性模型在乙脑和气象因素关系探讨中的应用,并为气象因素在乙脑预警模型中的应用提供依据。方法通过分布滞后非线性模型,探索2004-2010年期间贵州省11个区县,乙脑和气象因素的关系。结果随着周总降雨量的增加,其有效滞后范围从第6~7周,逐渐扩大到第2~7周,其相对危险度的峰值出现在第2周或第7周。随着周平均最低温度的升高,其有效滞后范围从第6周,逐渐扩大到第2~3周和第5~7周,其相对危险度的峰值出现在第2周或第6周。当周总降雨量取值为其50%和75%百分数时,其相对危险度峰值分别是1.10(95%CI:1.05~1.15)和1.22(95%CI:1.09~1.35)。当周平均最低温度取值为其50%和75%百分数时,其相对危险度峰值分别是2.39(95%CI:1.21~4.69)和7.99(95%CI:2.17~29.47)。结论分布滞后非线性模型精确详细地描述了气象因素在乙脑传播过程中的滞后特点。更多还原

【Abstract】 Objective The study aimed to promote the application of the distributed lag non-linear model in relation between epidemic encephalitis B andmeteorological factors, so as to provide a reference for the application of meteorological factors on early-warning model about epidemic encephalitis B. Methods The relation between epidemic encephalitis B andmeteorological factors in 11 districts and counties of Guizhou Province between 2004 and 2010 were explored by distributed lag non-linear model. Results With the increase of total weekly precipitation, the effective lag range expanded from 6th-7thweeks to 2nd-7thweeks gradually with the peak of relative risk in 2ndweek or 7thweek. With the rising of weekly average minimal temperature, the lag range gradually expanded from 6th week to 2nd-3rdweeks and 5th-7thweeks with the peak of relative risk in 2ndweek or 6th week. For 50% and 75% of the total weekly precipitation, the peaks of relative risk were 1.10(95% CI: 1.05-1.15) and 1.22(95% CI: 1.05-1.15), respectively. For 50% and 75% of the weekly average minimal temperature,the peaks of relative risk were2.39(95% CI: 1.21-4.69)and 7.99(95% CI:2.17-29.47), respectively. Conclusion The distributed lag non-linear model precisely describes the lag features of meteorological factors for transmission of epidemic encephalitis B.更多还原