Ranking of the Territory of Sochi by the Risk of Infection with HFRS Using the Method of Maximum Entropy

E. V. Chehvalova, E. Manin, A. Kulichenko
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Abstract

Relevance. Hemorrhagic fever with renal syndrome (HFRS), due to the severity of the course and high mortality, poses a serious threat to the health of the population of both the city of Sochi and its guests. Therefore, in order to implement more effective and less economically costly anti-epidemic and preventive measures, it is very important to constantly monitor the activity of the natural focus of HFRS, as well as to have a clear idea of the territories most dangerous for the risk of infection with this infection.Aims. Assessment of the epidemiological significance of the territory of the city of Sochi for the risk of infection with HFRS based on the maximum entropy method using a geographical information system.Materials & Methods. Based on the application of the maximum entropy algorithm implemented in the MaxEnt program, as well as the ArcGIS 10 program.8. Ranking of the territory of the city of Sochi according to the risk of infection with HFRS was performed. The paper uses: data on positive epizootological findings (a total of 131) for 2016-2021, which were obtained from the Sochi branch of the Federal Medical Institution «Center of Hygiene and Epidemiology in the Krasnodar Territory», the Sochi branch of the Federal Medical Institution «Black Sea Plague Station» of Rospotrebnadzor, as well as the Stavropol Plague Control Research Institute of the Rospotrebnadzor; materials on environmental conditions from the Biolclim data bank, vegetation index for 9 months (https://land.copernicus.eu/global/products/NDVI ). Preliminary preparation of the information was carried out using the ArcGIS 10 program.8. As a tool for building a training model, the MaxEnt program version 3.4.4 was used (https://biodiversityinformatics.amnh.org/open_source/maxent /).Results and discussion. The practical implementation of the tasks was to obtain maps of the epidemiological significance of the territory for the risk of infection with HFRS by superimposing the points of occurrence of the species (reservoir and carrier of HFRS) on maps of abiotic environmental factors affecting its spread. The implementation of this work consisted of the sequential implementation of four main stages: the first – the collection, generalization and transformation of bioclimatic and epizootic-epidemiological data; the second – the selection of the most significant data for the construction of the model; the third – the ranking of the territory of the city. Sochi on the risk of the spread of HFRS using GIS; the fourth is the analysis of the data obtained. In the course of the work, a model was obtained that allows dividing the study area according to the degree of risk of infection with HFRS with a high degree of reliability and significant prognostic value.Conclusions. The use of the model makes it possible to obtain new, more detailed data from a spatial point of view on the boundaries of potentially dangerous sites in the region in terms of GLPS. In particular, this applies to those territories where positive epizootological findings and cases of infection with HFRS have not been previously noted.
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用最大熵法对索契地区感染 HFRS 的风险进行排序
相关性。肾综合征出血热(HFRS)由于病程长、死亡率高,对索契市居民及其客人的健康构成严重威胁。因此,为了采取更有效、经济成本更低的抗疫和预防措施,必须持续监测肾病综合征自然病灶的活动情况,并清楚地了解哪些地区最容易感染这种疾病。利用地理信息系统,基于最大熵法评估索契市境内 HFRS 感染风险的流行病学意义。根据最大熵算法在 MaxEnt 程序和 ArcGIS 10 程序中的应用,对索契市境内感染 HFRS 的风险进行了排序。本文使用的数据包括:2016-2021 年的流行病学阳性结果数据(共 131 项),这些数据来自联邦医疗机构 "克拉斯诺达尔边疆区卫生与流行病学中心 "索契分部、俄联邦医疗机构 "黑海鼠疫站 "索契分部以及俄联邦医疗机构斯塔夫罗波尔鼠疫控制研究所;来自 Biolclim 数据库的环境条件资料、9 个月的植被指数 (https://land.copernicus.eu/global/products/NDVI )。信息的初步准备工作使用 ArcGIS 10 程序进行。作为建立训练模型的工具,使用了 MaxEnt 程序 3.4.4 版 (https://biodiversityinformatics.amnh.org/open_source/maxent /)。结果与讨论。任务的实际执行是通过在影响 HFRS 传播的非生物环境因素地图上叠加该物种(HFRS 的储库和载体)的发生点,获得该地区感染 HFRS 风险的流行病学意义地图。这项工作的实施包括四个主要阶段的连续实施:第一阶段--收集、归纳和转换生物气候和流行病学数据;第二阶段--选择最重要的数据用于构建模型;第三阶段--对城市领土进行排序。第三,利用地理信息系统对索契市境内的 HFRS 传播风险进行排序;第四,对获得的数据进行分析。在工作过程中获得了一个模型,该模型可根据感染 HFRS 的风险程度划分研究区域,具有高度可靠性和显著的预后价值。通过使用该模型,可以从空间角度获得新的、更详细的数据,以了解该地区在 GLPS 方面具有潜在危险的地点的边界。这尤其适用于那些以前未发现过阳性动物学发现和感染 HFRS 病例的地区。
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