Different environmental factors predict the occurrence of tick-borne encephalitis virus (TBEV) and reveal new potential risk areas across Europe via geospatial models.

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Patrick H Kelly, Rob Kwark, Harrison M Marick, Julie Davis, James H Stark, Harish Madhava, Gerhard Dobler, Jennifer C Moïsi
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引用次数: 0

Abstract

Background: Tick-borne encephalitis (TBE) is the most serious tick-borne viral disease in Europe. Identifying TBE risk areas can be difficult due to hyper focal circulation of the TBE virus (TBEV) between mammals and ticks. To better define TBE hazard risks and elucidate regional-specific environmental factors that drive TBEV circulation, we developed two machine-learning (ML) algorithms to predict the habitat suitability (maximum entropy), and occurrence of TBEV (extreme gradient boosting) within distinct European regions (Central Europe, Nordics, and Baltics) using local variables of climate, habitat, topography, and animal hosts and reservoirs.

Methods: Geocoordinates that reported the detection of TBEV in ticks or rodents and anti-TBEV antibodies in rodent reservoirs in 2000 or later were extracted from published and grey literature. Region-specific ML models were defined via K-means clustering and trained according to the distribution of extracted geocoordinates relative to explanatory variables in each region. Final models excluded colinear variables and were evaluated for performance.

Results: 521 coordinates (455 ticks; 66 rodent reservoirs) of TBEV occurrence (2000-2022) from 100 records were extracted for model development. The models had high performance across regions (AUC: 0.72-0.92). The strongest predictors of habitat suitability and TBEV occurrence in each region were associated with different variable categories: climate variables were the strongest predictors of habitat suitability in Central Europe; rodent reservoirs and elevation were strongest in the Nordics; and animal hosts and land cover contributed most to the Baltics. The models predicted several areas with few or zero reported TBE incidence as highly suitable (≥ 60%) TBEV habitats or increased probability (≥ 25%) of TBEV occurrence including western Norway coastlines, northern Denmark, northeastern Croatia, eastern France, and northern Italy, suggesting potential capacity for locally-acquired autochthonous TBEV infections or possible underreporting of TBE cases based on reported human surveillance data.

Conclusions: This study shows how varying environmental factors drive the occurrence of TBEV within different European regions and identifies potential new risk areas for TBE. Importantly, we demonstrate the utility of ML models to generate reliable insights into TBE hazard risks when trained with sufficient explanatory variables and to provide high resolution and harmonized risk maps for public use.

不同的环境因素预测了蜱传脑炎病毒(TBEV)的发生,并通过地理空间模型揭示了欧洲新的潜在风险区域。
背景:蜱传脑炎(TBE)是欧洲最严重的蜱传病毒性疾病。由于TBE病毒(TBEV)在哺乳动物和蜱之间的高度集中循环,确定TBE风险区域可能很困难。为了更好地定义TBEV危害风险并阐明驱动TBEV循环的区域特定环境因素,我们开发了两种机器学习(ML)算法来预测欧洲不同地区(中欧、北欧和波罗的海)的栖息地适宜性(最大熵)和TBEV(极端梯度增强)的发生,使用当地的气候、栖息地、地形、动物宿主和水库变量。方法:从已发表文献和灰色文献中提取2000年及以后报道蜱、鼠类中检测到TBEV和鼠库中抗TBEV抗体的地理坐标。通过K-means聚类定义特定区域的ML模型,并根据提取的地理坐标相对于每个区域的解释变量的分布进行训练。最终模型排除了共线性变量,并对其性能进行了评估。结果:521坐标(455刻度;从100条记录中提取了66个鼠类储层(2000-2022年)的TBEV发生情况,用于模型开发。各模型均具有较高的区域性能(AUC: 0.72 ~ 0.92)。各区域的生境适宜性和TBEV发生的最强预测因子与不同的变量类别相关:气候变量是中欧地区生境适宜性的最强预测因子;北欧鼠库和海拔最高;动物宿主和土地覆盖对波罗的海贡献最大。该模型预测了几个报告发病率很少或为零的地区为高适宜(≥60%)TBEV栖息地或TBEV发生概率增加(≥25%)的地区,包括挪威西部海岸线、丹麦北部、克罗地亚东北部、法国东部和意大利北部,这表明当地获得性本地TBEV感染的潜在能力,或根据报告的人类监测数据可能漏报了TBEV病例。结论:本研究显示了不同的环境因素如何在不同的欧洲地区驱动TBEV的发生,并确定了潜在的新的TBE风险区域。重要的是,我们展示了机器学习模型的实用性,当有足够的解释变量训练时,它可以生成对be危害风险的可靠见解,并提供高分辨率和统一的风险图供公众使用。
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来源期刊
International Journal of Health Geographics
International Journal of Health Geographics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH -
CiteScore
10.20
自引率
2.00%
发文量
17
审稿时长
12 weeks
期刊介绍: A leader among the field, International Journal of Health Geographics is an interdisciplinary, open access journal publishing internationally significant studies of geospatial information systems and science applications in health and healthcare. With an exceptional author satisfaction rate and a quick time to first decision, the journal caters to readers across an array of healthcare disciplines globally. International Journal of Health Geographics welcomes novel studies in the health and healthcare context spanning from spatial data infrastructure and Web geospatial interoperability research, to research into real-time Geographic Information Systems (GIS)-enabled surveillance services, remote sensing applications, spatial epidemiology, spatio-temporal statistics, internet GIS and cyberspace mapping, participatory GIS and citizen sensing, geospatial big data, healthy smart cities and regions, and geospatial Internet of Things and blockchain.
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