The Experience in Using the MaxEnt Model to Rank the Territory of the Caspian Sandy Natural Plague Focus (43) according to the Risk of Epizooty Registration

U. Ashibokov, V. Dubyansky, O. Semenko, A. Gazieva, O. A. Belova, A. A. Kes’yan, A. K. Khalidov, A. A. Vetoshkin, N. V. Viktorova, A. A. Kulik
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Abstract

The aim of this work was to rank the territory of the Caspian sandy natural plague focus (43) by the risk of epizooty emergence using the MaxEnt model.Materials and methods. The archival data on epizootic manifestations of plague over the past 35 years, aggregated by the Stavropol Anti-Plague Institute of the Rospotrebnadzor, the Dagestan, Elista, Astrakhan PCSs of the Rospotrebnadzor, were used for model design. 615 archive plague detection points were converted into the coordinate system (1980–2015). 87 publicly available bioclimatic variables BioClim were deployed to construct the MaxEnt model. Applied weather and climatic factors of the BioClim database are averaged over a multiyear period.Results and discussion. The MaxEnt model has a very high degree of reliability (AUC=0.975), with a sufficiently high predictive ability (AUC=0.973). According to the generated model, the Caspian sandy natural plague focus has a heterogeneous structure in terms of the probability of epizooty registration and can be divided into five zones. The most significant factors for the model are the following indicators: the average temperature of the wettest quarter, solar radiation in November, the average temperature of the driest quarter, the amount of precipitation in the coldest quarter, wind speed in May, the amount of precipitation in the wettest quarter, and the average air temperature in September. The data obtained allow for targeted search for plague epizootics and can be used to adjust boundaries of a surveyed natural focus in the future.
使用 MaxEnt 模型根据流行病登记风险对里海沙地自然鼠疫重点地区(43)进行排序的经验
这项工作的目的是利用 MaxEnt 模型,根据鼠疫流行风险对里海沙地自然鼠疫疫点(43 个)进行排序。在设计模型时,使用了俄罗斯国家边防局斯塔夫罗波尔市鼠疫防治研究所、俄罗斯国家边防局达吉斯坦市、埃利斯塔市、阿斯特拉罕州鼠疫防治中心在过去 35 年中收集的鼠疫流行病表现档案数据。将 615 个鼠疫档案检测点转换为坐标系(1980-2015 年)。87 个公开可用的生物气候变量 BioClim 被用于构建 MaxEnt 模型。BioClim 数据库中应用的天气和气候因子是多年平均值。MaxEnt 模型具有很高的可靠性(AUC=0.975)和足够高的预测能力(AUC=0.973)。根据生成的模型,里海沙地自然鼠疫疫点在流行病登记概率方面具有异质性结构,可划分为五个区域。模型中最重要的因素包括以下指标:最潮湿季度的平均气温、11 月份的太阳辐射、最干旱季度的平均气温、最寒冷季度的降水量、5 月份的风速、最潮湿季度的降水量以及 9 月份的平均气温。获得的数据可用于有针对性地寻找鼠疫流行区,并可用于今后调整调查自然焦点的边界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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