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捷克共和国(南波希米亚)和德国(下巴伐利亚和上普法尔茨)边境地区莱姆病螺旋体和 tick-borne 脑炎病毒感染 tick 的风险暴露模型。

Model of Risk of Exposure to Lyme Borreliosis and Tick-Borne Encephalitis Virus-Infected Ticks in the Border Area of the Czech Republic (South Bohemia) and Germany (Lower Bavaria and Upper Palatinate).

机构信息

Institute of Parasitology, Biology Centre, Academy of Sciences of Czech Republic, Branisovska 31, 370 05 Ceske Budejovice, Czech Republic.

Faculty of Science, University of South Bohemia, Branisovska 31, 370 05 Ceske Budejovice, Czech Republic.

出版信息

Int J Environ Res Public Health. 2019 Apr 2;16(7):1173. doi: 10.3390/ijerph16071173.

Abstract

In Europe, Lyme borreliosis (LB) and tick-borne encephalitis (TBE) are the two vector-borne diseases with the largest impact on human health. Based on data on the density of host-seeking ticks and pathogen prevalence and using a variety of environmental data, we have created an acarological risk model for a region where both diseases are endemic (Czech Republic-South Bohemia and Germany-Lower Bavaria, Upper Palatinate). The data on tick density were acquired by flagging 50 sampling sites three times in a single season. Prevalence of the causative agents of LB and TBE was determined. Data on environmental variables (e.g., altitude, vegetation cover, NDVI, land surface temperature) were obtained from various sources and processed using geographical information systems. Generalized linear models were used to estimate tick density, probability of tick infection, and density of infected ticks for the whole area. A significantly higher incidence of human TBE cases was recorded in South Bohemia compared to Bavarian regions, which correlated with a lower tick density in Bavaria. However, the differences in pathogen prevalence rates were not significant. The model outputs were made available to the public in the form of risk maps, indicating the distribution of tick-borne disease risk in space.

摘要

在欧洲,莱姆病(LB)和蜱传脑炎(TBE)是对人类健康影响最大的两种虫媒病。基于宿主寻食蜱密度、病原体流行率以及各种环境数据,我们为两个疾病流行地区(捷克共和国南波希米亚和德国巴伐利亚低地、上普法尔茨)创建了一种节肢动物风险模型。蜱密度数据是通过在一个季节内三次标记 50 个采样点获得的。LB 和 TBE 的病原体流行率是通过检测确定的。环境变量(如海拔、植被覆盖、NDVI、地表温度)的数据来自不同来源,并使用地理信息系统进行处理。广义线性模型用于估算整个地区的蜱密度、蜱感染概率和感染蜱密度。与巴伐利亚地区相比,南波希米亚地区的人类 TBE 病例发生率明显更高,这与巴伐利亚地区较低的蜱密度有关。然而,病原体流行率的差异并不显著。模型输出以风险图的形式提供给公众,显示了空间上蜱传疾病风险的分布。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93ac/6479554/7cf55b0afc22/ijerph-16-01173-g001.jpg

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