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瑞士基于风险的蚊虫监测与控制工具。

Risk-Based Mapping Tools for Surveillance and Control of the Invasive Mosquito in Switzerland.

机构信息

Department for Environment Constructions and Design, Institute of Microbiology (IM), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6850 Mendrisio, Switzerland.

Department of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence Studies (IDSIA), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6962 Lugano-Viganello, Switzerland.

出版信息

Int J Environ Res Public Health. 2022 Mar 9;19(6):3220. doi: 10.3390/ijerph19063220.

Abstract

BACKGROUND

In Switzerland, is well established in Ticino, south of the Alps, where surveillance and control are implemented. The mosquito has also been observed in Swiss cities north of the Alps. Decision-making tools are urgently needed by the local authorities in order to optimize surveillance and control.

METHODS

A regularized logistic regression was used to link the long-term dataset of occurrence in Ticino with socioenvironmental predictors. The probability of establishment of was extrapolated to Switzerland and more finely to the cities of Basel and Zurich.

RESULTS

The model performed well, with an AUC of 0.86. Ten socio-environmental predictors were selected as informative, including the road-based distance in minutes of travel by car from the nearest cell established in the previous year. The risk maps showed high suitability for establishment in the Central Plateau, the area of Basel, and the lower Rhone Valley in the Canton of Valais.

CONCLUSIONS

The areas identified as suitable for establishment are consistent with the actual current findings of tiger mosquito. Our approach provides a useful tool to prompt authorities' intervention in the areas where there is higher risk of introduction and establishment of .

摘要

背景

在瑞士阿尔卑斯山以南的提契诺州,已经建立了蚊媒监测和控制体系,在那里得到了很好的控制。在阿尔卑斯山以北的瑞士城市也已经观察到了这种蚊子。为了优化监测和控制,当地政府迫切需要决策工具。

方法

我们使用正则化逻辑回归将提契诺州的 发生的长期数据集与社会环境预测因子联系起来。将 的建立概率外推到瑞士,并更精细地外推到巴塞尔和苏黎世市。

结果

该模型表现良好,AUC 为 0.86。选择了 10 个社会环境预测因子作为信息丰富的预测因子,包括与上一年建立的最近细胞之间的汽车行驶分钟数的基于道路的距离。风险图显示出在中央高原、巴塞尔地区和瓦莱州的罗纳河谷下游地区建立 的高度适宜性。

结论

确定的适合 建立的区域与实际发现的虎蚊相吻合。我们的方法为当局在引入和建立 的风险较高的地区提供了一个有用的干预工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b99/8955472/7ec3f9f6d3c3/ijerph-19-03220-g0A1.jpg

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