Department of Biogeography, University of Bayreuth, Universitaetsstr. 30, 95447, Bayreuth, Germany.
Bayreuth Center of Ecology and Environmental Research, BayCEER, University of Bayreuth, Universitaetsstr. 30, 95447, Bayreuth, Germany.
J Epidemiol Glob Health. 2024 Sep;14(3):1052-1070. doi: 10.1007/s44197-024-00254-0. Epub 2024 Jul 4.
Since the first autochthonous transmission of West Nile Virus was detected in Germany (WNV) in 2018, it has become endemic in several parts of the country and is continuing to spread due to the attainment of a suitable environment for vector occurrence and pathogen transmission. Increasing temperature associated with a changing climate has been identified as a potential driver of mosquito-borne disease in temperate regions. This scenario justifies the need for the development of a spatially and temporarily explicit model that describes the dynamics of WNV transmission in Germany. In this study, we developed a process-based mechanistic epidemic model driven by environmental and epidemiological data. Functional traits of mosquitoes and birds of interest were used to parameterize our compartmental model appropriately. Air temperature, precipitation, and relative humidity were the key climatic forcings used to replicate the fundamental niche responsible for supporting mosquito population and infection transmission risks in the study area. An inverse calibration method was used to optimize our parameter selection. Our model was able to generate spatially and temporally explicit basic reproductive number (R) maps showing dynamics of the WNV occurrences across Germany, which was strongly associated with the deviation from daily means of climatic forcings, signaling the impact of a changing climate in vector-borne disease dynamics. Epidemiological data for human infections sourced from Robert Koch Institute and animal cases collected from the Animal Diseases Information System (TSIS) of the Friedrich-Loeffler-Institute were used to validate model-simulated transmission rates. From our results, it was evident that West Nile Virus is likely to spread towards the western parts of Germany with the rapid attainment of environmental suitability for vector mosquitoes and amplifying host birds, especially short-distance migratory birds. Locations with high risk of WNV outbreak (Baden-Württemberg, Bavaria, Berlin, Brandenburg, Hamburg, North Rhine-Westphalia, Rhineland-Palatinate, Saarland, Saxony-Anhalt and Saxony) were shown on R maps. This study presents a path for developing an early warning system for vector-borne diseases driven by climate change.
自 2018 年德国首次检测到西尼罗河病毒（WNV）本土传播以来，该病毒已在该国多个地区流行，并因适合媒介发生和病原体传播的环境而持续传播。与气候变化相关的温度升高已被确定为温带地区蚊媒疾病的潜在驱动因素。这种情况证明有必要开发一种具有空间和时间明确性的模型，以描述德国 WNV 传播的动态。在这项研究中，我们开发了一个基于过程的机制流行病模型，该模型由环境和流行病学数据驱动。感兴趣的蚊子和鸟类的功能特征被用于适当参数化我们的隔间模型。空气温度、降水和相对湿度是用于复制研究区域内支持蚊子种群和感染传播风险的基本生态位的关键气候强迫因素。反演校准方法用于优化我们的参数选择。我们的模型能够生成具有空间和时间明确性的基本繁殖数（R）地图，显示德国各地 WNV 发生的动态，这些动态与气候强迫因素的日均值偏差密切相关，表明气候变化对媒介传播疾病动态的影响。从罗伯特·科赫研究所（Robert Koch Institute）获得的人类感染流行病学数据和从弗里德里希·洛夫勒研究所（Friedrich-Loeffler-Institute）的动物疾病信息系统（TSIS）收集的动物病例用于验证模型模拟的传播率。从我们的结果可以明显看出，随着适合媒介蚊子和扩增宿主鸟类（尤其是短距离候鸟）的环境快速获得，西尼罗河病毒很可能向德国西部传播。高 WNV 爆发风险的地点（巴登-符腾堡州、巴伐利亚州、柏林、勃兰登堡州、汉堡、北莱茵-威斯特法伦州、莱茵兰-普法尔茨州、萨尔州、萨克森-安哈尔特州和萨克森州）在 R 地图上显示。这项研究为气候变化驱动的虫媒疾病预警系统的开发提供了一条途径。
