Department of Mathematics and Mathematical Statistics, Computational Life Science Cluster, Umeå University, Sweden.
J Infect Dis. 2012 Jan 15;205(2):297-304. doi: 10.1093/infdis/jir732. Epub 2011 Nov 28.
We aimed to evaluate the potential association of mosquito prevalence in a boreal forest area with transmission of the bacterial disease tularemia to humans, and model the annual variation of disease using local weather data.
A prediction model for mosquito abundance was built using weather and mosquito catch data. Then a negative binomial regression model based on the predicted mosquito abundance and local weather data was built to predict annual numbers of humans contracting tularemia in Dalarna County, Sweden.
Three hundred seventy humans were diagnosed with tularemia between 1981 and 2007, 94% of them during 7 summer outbreaks. Disease transmission was concentrated along rivers in the area. The predicted mosquito abundance was correlated (0.41, P < .05) with the annual number of human cases. The predicted mosquito peaks consistently preceded the median onset time of human tularemia (temporal correlation, 0.76; P < .05). Our final predictive model included 5 environmental variables and identified 6 of the 7 outbreaks.
This work suggests that a high prevalence of mosquitoes in late summer is a prerequisite for outbreaks of tularemia in a tularemia-endemic boreal forest area of Sweden and that environmental variables can be used as risk indicators.
我们旨在评估在北方森林地区蚊子流行与人类细菌疾病土拉菌病传播之间的潜在关联，并使用当地气象数据对疾病的年度变化进行建模。
使用气象和蚊子捕获数据构建蚊子丰度预测模型。然后，基于预测的蚊子丰度和当地气象数据，构建了基于负二项回归模型的瑞典达拉纳县人类感染土拉菌病的年度人数预测模型。
1981 年至 2007 年间，有 370 人被诊断患有土拉菌病，其中 94%发生在 7 次夏季暴发期间。疾病传播集中在该地区的河流沿岸。预测的蚊子丰度与人类病例的年度数量相关（0.41，P<.05）。预测的蚊子高峰始终先于人类土拉菌病的中位数发病时间（时间相关性，0.76；P<.05）。我们的最终预测模型包括 5 个环境变量，并确定了 7 次暴发中的 6 次。
这项工作表明，在瑞典土拉菌病流行的北方森林地区，夏末蚊子高发是土拉菌病暴发的前提条件，环境变量可用作风险指标。
