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首页> 外文期刊>BMC Infectious Diseases >Tick-borne encephalitis (TBE) cases are not random: explaining trend, low- and high-frequency oscillations based on the Austrian TBE time series
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Tick-borne encephalitis (TBE) cases are not random: explaining trend, low- and high-frequency oscillations based on the Austrian TBE time series

机译:蜱型脑炎(TBE)案例不随意:解释基于奥地利TBE时间序列的趋势,低频和高频振荡

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BACKGROUND:Why human tick-borne encephalitis (TBE) cases differ from year to year, in some years more 100%, has not been clarified, yet. The cause of the increasing or decreasing trends is also controversial. Austria is the only country in Europe where a 40-year TBE time series and an official vaccine coverage time series are available to investigate these open questions.METHODS:A series of generalized linear models (GLMs) has been developed to identify demographic and environmental factors associated with the trend and the oscillations of the TBE time series. Both the observed and the predicted TBE time series were subjected to spectral analysis. The resulting power spectra indicate which predictors are responsible for the trend, the high-frequency and the low-frequency oscillations, and with which explained variance they contribute to the TBE oscillations.RESULTS:The increasing trend can be associated with the demography of the increasing human population. The responsible GLM explains 12% of the variance of the TBE time series. The low-frequency oscillations (10 years) are associated with the decadal changes of the large-scale climate in Central Europe. These are well described by the so-called Scandinavian index. This 10-year oscillation cycle is reinforced by the socio-economic predictor net migration. Considering the net migration and the Scandinavian index increases the explained variance of the GLM to 44%. The high-frequency oscillations (2-3 years) are associated with fluctuations of the natural TBE transmission cycle between small mammals and ticks, which are driven by beech fructification. Considering also fructification 2 years prior explains 64% of the variance of the TBE time series. Additionally, annual sunshine duration as predictor for the human outdoor activity increases the explained variance to 70%.CONCLUSIONS:The GLMs presented here provide the basis for annual TBE forecasts, which were mainly determined by beech fructification. A total of 3 of the 5 years with full fructification, resulting in high TBE case numbers 2 years later, occurred after 2010. The effects of climate change are therefore not visible through a direct correlation of the TBE cases with rising temperatures, but indirectly via the increased frequency of mast seeding.
机译:背景:为什么人类蜱的脑炎(TBE)案件与年内不同,在多年来,尚未澄清,尚未澄清。趋势越来越长或减少的原因也是有争议的。奥地利是欧洲唯一的国家,其中40年的时间序列和官方疫苗覆盖时间序列可以调查这些打开的问题。已经开发出一系列广义的线性模型(GLM)以识别人口统计和环境因素与TBE时间序列的趋势和振荡相关联。观察到的和预测的TBE时间序列都经受光谱分析。得到的功率谱指示哪些预测器对趋势,高频和低频振荡负责,并且其解释了它们对Tbe振荡的贡献。结果:增加的趋势可以与增加的人口统计相关联人口。负责GLM解释了TBE时间序列方差的12%。低频振荡(10年)与中欧大规模气候的二等变化有关。这些是由所谓的斯堪的纳维亚指数良好的描述。该10年的振荡周期被社会经济预测净迁移加强。考虑到净移民和斯堪的纳维亚指数增加了GLM的解释方差至44%。高频振荡(2-3岁)与小型哺乳动物和蜱之间的天然TBE传输周期的波动有关,这是由山毛榉果实驱动的。考虑到2年的果实2年来解释了TBE时间序列的64%的差异。此外,人类户外活动的预测因子的年阳光持续时间将解释的差异增加到70%.Conclusions:这里提出的GLMS提供了每年TBE预测的基础,主要由Beech果实决定。总共有5年的果实中的3年,导致2年后的TBE案例数,在2010年后发生。因此,通过直接相关的气候变化的影响,通过对案件的温度的直接相关,而是间接通过桅杆播种频率增加。

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