Huitink Maartje, de Rooij Myrna, Montarsi Fabrizio, Salvati Maria Vittoria, Obber Federica, Da Rold Graziana, Sgubin Sofia, Mazzotta Elisa, di Martino Guido, Mazzucato Matteo, Salata Cristiano, Vonesch Nicoletta, Tomao Paola, Mughini-Gras Lapo
Institute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 2, 3584 CL Utrecht, The Netherlands.
Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Viale dell'Università 10, 35020 Legnaro, Italy.
Pathogens. 2024 Sep 27;13(10):836. doi: 10.3390/pathogens13100836.
ticks are ubiquitous in Europe, including in North-East Italy. These ticks are important vectors of several zoonotic pathogens of public health relevance. In this study, the habitat suitability range of ticks infected with zoonotic pathogens was predicted in North-East Italy, and relevant spatial predictors were identified. In 2015-2021, ticks were collected at 26 sampling sites in the study area. The collected ticks were screened for the presence of pathogens using PCR assays. For , and species, data allowed for ecological niche modelling using Maxent. Environmental determinants potentially related to tick habitat suitability were used as model inputs. Predicted suitable habitat distributions revealed hotspots of the probability of pathogen presence in ticks mainly in the central and upper parts of the study area. Key environmental predictors were temperature, rainfall and altitude, and vegetation index for specific pathogens ( and / species). Increased risk of exposure to tick-borne pathogens upon tick bites in the predicted hotspot areas can, therefore, be expected. This provides useful information for public health risk managers in this and other similar regions.
蜱虫在欧洲广泛存在，包括意大利东北部。这些蜱虫是几种具有公共卫生相关性的人畜共患病原体的重要传播媒介。在本研究中，预测了意大利东北部感染人畜共患病原体的蜱虫的栖息地适宜范围，并确定了相关的空间预测因子。2015年至2021年期间，在研究区域的26个采样点采集了蜱虫。使用聚合酶链反应（PCR）检测对采集的蜱虫进行病原体筛查。对于[具体蜱虫种类1]、[具体蜱虫种类2]和[具体蜱虫种类3]，数据允许使用最大熵模型（Maxent）进行生态位建模。将可能与蜱虫栖息地适宜性相关的环境决定因素用作模型输入。预测的适宜栖息地分布显示，病原体在蜱虫中存在的概率热点主要位于研究区域的中部和上部。关键的环境预测因子是温度、降雨量和海拔，以及特定病原体（[具体病原体1]和[具体病原体2]/[具体蜱虫种类]）的植被指数。因此，预计在预测的热点地区蜱虫叮咬后接触蜱传病原体的风险会增加。这为该地区及其他类似地区的公共卫生风险管理人员提供了有用信息。
