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影响北达科他州伯利县西尼罗河病毒发病率的关键因素。

Key Factors Influencing the Incidence of West Nile Virus in Burleigh County, North Dakota.

机构信息

Environmental Science Graduate Program, The Ohio State University, Columbus, OH 43210, USA.

College of Public Health, The Ohio State University, Columbus, OH 43210, USA.

出版信息

Int J Environ Res Public Health. 2018 Sep 5;15(9):1928. doi: 10.3390/ijerph15091928.

Abstract

The city of Bismarck, North Dakota has one of the highest numbers of West Nile Virus (WNV) cases per population in the U.S. Although the city conducts extensive mosquito surveillance, the mosquito abundance alone may not fully explain the occurrence of WNV. Here, we developed models to predict mosquito abundance and the number of WNV cases, independently, by statistically analyzing the most important climate and virus transmission factors. An analysis with the mosquito model indicated that the mosquito numbers increase during a warm and humid summer or after a severely cold winter. In addition, river flooding decreased the mosquito numbers. The number of WNV cases was best predicted by including the virus transmission rate, the mosquito numbers, and the mosquito feeding pattern. This virus transmission rate is a function of temperature and increases significantly above 20 °C. The correlation coefficients () were 0.910 with the mosquito-population model and 0.620 with the disease case model. Our findings confirmed the conclusions of other work on the importance of climatic variables in controlling the mosquito numbers and contributed new insights into disease dynamics, especially in relation to extreme flooding. It also suggested a new prevention strategy of initiating insecticides not only based on mosquito numbers but also 10-day forecasts of unusually hot weather.

摘要

北达科他州俾斯麦市的西尼罗河病毒(WNV)病例数在全美人口中最高。尽管该市进行了广泛的蚊子监测,但蚊子数量本身可能并不能完全解释 WNV 的发生。在这里,我们通过统计分析最重要的气候和病毒传播因素,分别建立了预测蚊子数量和 WNV 病例数的模型。蚊子数量模型的分析表明,在温暖潮湿的夏季或严寒的冬季后,蚊子数量会增加。此外,河流洪水会减少蚊子数量。包含病毒传播率、蚊子数量和蚊子吸血模式的分析可以最好地预测 WNV 病例数。该病毒传播率是温度的函数,在 20°C 以上会显著增加。与蚊子种群模型的相关系数(r)为 0.910,与疾病模型的相关系数为 0.620。我们的研究结果证实了其他关于气候变量在控制蚊子数量方面重要性的研究结论,并为疾病动态学提供了新的见解,特别是与极端洪水有关的见解。这也表明了一种新的预防策略,即不仅根据蚊子数量,还根据异常炎热天气的 10 天预测来启动杀虫剂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1879/6164257/d3c806e2dbec/ijerph-15-01928-g001.jpg

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