4.7 Article

Spatiotemporal Patterns of African Swine Fever in Wild Boar in the Russian Federation (2007-2022): Using Clustering Tools for Revealing High-Risk Areas

Journal

ANIMALS
Volume 13, Issue 19, Pages -

Publisher

MDPI
DOI: 10.3390/ani13193081

Keywords

wild boar; animal population density; African swine fever; hot spot analysis; spatiotemporal clustering; SaTScan; Russia

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In this study, we used spatiotemporal analysis to identify the areas in the Russian Federation where an increased density of wild boar population is associated with a higher concentration of African swine fever (ASF) cases. We found areas where the wild boar population density has been continuously rising in recent years, which may be considered high-risk areas requiring enhanced surveillance and population control measures.
Simple Summary In this study, we utilized the methods of spatiotemporal analysis to reveal and visualize the areas in the Russian Federation where an increased density of wild boar population might be related to the concentration of African swine fever (ASF) cases in wild boar. We demonstrated the areas (at the municipality level), where the elevated wild boar population density has continued to rise in recent years, that may be treated as high-risk areas, subject to the application of enhanced surveillance and population control measures.Abstract African swine fever (ASF) is an infectious disease that affects both domestic pigs (DPs) and wild boar (WB). The WB population plays an important role in the spread of ASF as the WB acts as a natural reservoir of the virus and transmits it to other susceptible wild and domestic pigs. Our study was aimed at revealing the areas with a high concentration of the WB population, and their potential relationships with the grouping of ASF cases in WB during the course of the ASF spread in the Russian Federation (2007-2022). We collected the annual data on WB numbers by municipalities within the regions of the most intensive ASF spread. We then conducted spatiotemporal analysis to identify clustering areas of ASF cases and compare them with the territories with a high density of WB population. We found that some of the territories with elevated ASF incidence in WB demonstrated spatial and temporal coincidence with the areas with a high WB population density. We also visualized the zones (emerging hot spots) with a statistically significant rise in the WB population density in recent years, which may be treated as areas of paramount importance for the application of surveillance measures and WB population control.

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