目的 探讨上海市宝山区气象因素对媒介伊蚊密度指数的滞后性影响，为当地登革热预测预警提供依据。方法 采用分布滞后非线性模型（DLNM）分析2019年4－10月宝山区日平均气温、日最低气温、日最高气温、日平均气压、日平均风速、日平均相对湿度和日平均累计降雨量等气象因素对媒介伊蚊密度指数的滞后效应。结果 对帐诱指数的滞后效应上，日最低气温较高时，效应强度峰值较早出现在0～2 d，气温较低时，效应强度峰值较晚出现在20～30 d；对停落指数的滞后效应上，日平均气温较高时，效应强度峰值较早出现在0～5 d，气温较低时，效应强度峰值较晚出现在25～30 d；对布雷图指数的滞后效应上，日平均气温较高时，滞后效应强度峰值出现在最开始，气温较低时，效应强度峰值出现在5～10 d；对诱蚊诱卵指数的滞后效应上，日平均气温较高时，效应强度峰值较早出现在0～3 d，气温较低时，效应强度峰值较晚出现在15～30 d。结论 日平均气温、日平均相对湿度、日平均风速等气象因素对于上海市宝山区伊蚊密度存在滞后效应，高温、高湿、较高或较低风速，可加快伊蚊密度滞后效应峰值的出现，结果提示基于气象因素可预测伊蚊密度变化。
Objective To investigate the lag effects of meteorological factors on the density indices of Aedes mosquitoes in Baoshan district, Shanghai, China, and to provide a basis for early warning and prediction for local dengue fever epidemic. Methods A distributed lag non-linear model was used to analyze the lag effects of meteorological factors on Aedes density indices in Baoshan district from April to October, 2019, including daily average temperature, daily minimum temperature, daily maximum temperature, daily average air pressure, daily average wind speed, daily average relative humidity, and daily cumulative rainfall. Results For the net trap index, the lag effect peaked at shorter lags of 0 to 2 days when daily minimum temperature was high, and at longer lags of 20 to 30 days when the temperature was low. For the landing index, the lag effect peaked at shorter lags of 0 to 5 days when daily average temperature was high, and at longer lags of 25 to 30 days when the temperature was low. For the Breteau index, the lag effect peaked at the beginning when daily average temperature was high, and at lags of 5 to 10 days when the temperature was low. For the mosquito ovitrap index, the lag effect peaked at shorter lags of 0 to 3 days when daily average temperature was high, and at longer lags of 15 to 30 days when the temperature was low. Conclusion Meteorological factors, including daily average temperature, daily average relative humidity, and daily average wind speed, have lag effects on the density of Aedes mosquitoes in Baoshan district of Shanghai. High temperature and high humidity, with low or high wind speed, can accelerate the peak of the lag effect on Aedes density. Meteorological changes can be used to predict fluctuations in Aedes density.
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