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塑造人畜共患病风险:景观生态学与人对景观的吸引力——以瑞典蜱传脑炎为例

Shaping zoonosis risk: landscape ecology vs. landscape attractiveness for people, the case of tick-borne encephalitis in Sweden.

作者信息

Zeimes Caroline B, Olsson Gert E, Hjertqvist Marika, Vanwambeke Sophie O

机构信息

Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research (TECLIM), Université Catholique de Louvain (UCL), GEOG, Place Louis Pasteur 3 bte L4,03,07, 1348 Louvain-la-Neuve, Belgium.

出版信息

Parasit Vectors. 2014 Aug 15;7:370. doi: 10.1186/1756-3305-7-370.

Abstract

BACKGROUND

In this paper, the hazard and exposure concepts from risk assessment are applied in an innovative approach to understand zoonotic disease risk. Hazard is here related to the landscape ecology determining where the hosts, vectors and pathogens are and, exposure is defined as the attractiveness and accessibility to hazardous areas. Tick-borne encephalitis in Sweden was used as a case study.

METHODS

Three boosted regression tree models are compared: a hazard model, an exposure model and a global model which combines the two approaches.

RESULTS

The global model offers the best predictive power and the most accurate modelling. The highest probabilities were found in easy-to-reach places with high landscape diversity, holiday houses, waterbodies and, well-connected forests of oak, birch or pine, with open-area in their ecotones, a complex shape, numerous clear-cuts and, a variation in tree height.

CONCLUSION

While conditions for access and use of hazardous areas are quite specific to Scandinavia, this study offers promising perspectives to improve our understanding of the distribution of zoonotic and vector-borne diseases in diverse contexts.

摘要

背景

在本文中,风险评估中的危害和暴露概念被应用于一种创新方法,以理解人畜共患病风险。此处危害与景观生态学相关,景观生态学决定了宿主、媒介和病原体的所在位置,而暴露被定义为对危险区域的吸引力和可达性。瑞典的蜱传脑炎被用作案例研究。

方法

比较了三种增强回归树模型:危害模型、暴露模型和结合了这两种方法的全局模型。

结果

全局模型具有最佳的预测能力和最准确的建模效果。在景观多样性高、有度假屋、水体以及橡树、桦树或松树的连通性良好的森林(其交错带具有开阔区域、形状复杂、有大量皆伐区且树高有变化)的易到达地区,发现了最高的概率。

结论

虽然进入和使用危险区域的条件在斯堪的纳维亚半岛较为特殊,但本研究为增进我们对人畜共患病和媒介传播疾病在不同环境中分布的理解提供了有前景的视角。

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