Tick-borne pathogens cause diseases in animals and humans, and tick-borne disease incidence is increasing in many parts of the world. There is a need to assess the distribution of tick-borne pathogens and identify potential risk areas. We collected 29,440 tick nymphs from 50 sites in Scandinavia from August to September, 2016. We tested ticks in a real-time PCR chip, screening for 19 vector-associated pathogens. We analysed spatial patterns, mapped the prevalence of each pathogen and used machine learning algorithms and environmental variables to develop predictive prevalence models. All 50 sites had a pool prevalence of at least 33% for one or more pathogens, the most prevalent being Borrelia afzelii, B. garinii, Rickettsia helvetica, Anaplasma phagocytophilum, and Neoehrlichia mikurensis. There were large differences in pathogen prevalence between sites, but we identified only limited geographical clustering. The prevalence models performed poorly, with only models for R. helvetica and N. mikurensis having moderate predictive power (normalized RMSE from 0.74-0.75, R2 from 0.43-0.48). The poor performance of the majority of our prevalence models suggest that the used environmental and climatic variables alone do not explain pathogen prevalence patterns in Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area.

译文

T传播的病原体引起动物和人类疾病,tick传播的疾病发病率在世界许多地方都在增加。有必要评估tick传病原体的分布并确定潜在的危险区域。 2016年8月至2016年9月,我们从斯堪的那维亚的50个地点收集了29,440滴虫若虫。我们在实时PCR芯片中检测了滴虫,筛选了19种与载体相关的病原体。我们分析了空间格局,绘制了每种病原体的患病率,并使用了机器学习算法和环境变量来开发预测性患病率模型。对于一个或多个病原体,所有50个位点的合并患病率至少为33%,最普遍的是非洲疏螺旋体,加里氏芽孢杆菌,Heletica立克次体,吞噬嗜浆菌和mikurensis。站点之间的病原体流行率存在很大差异,但我们仅确定了有限的地理集群。患病率模型的执行情况较差,只有Helvetica和N. mikurensis的模型具有中等预测能力(RMSE的标准化值为0.74-0.75,R2的标准化值为0.43-0.48)。尽管我们以前的相同变量成功地预测了同一地区tick的空间格局,但大多数流行模型的不良表现表明,仅使用的环境和气候变量并不能解释斯堪的纳维亚半岛的病原体流行模式。

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