ArticleTransboundary and emerging diseases2025
Testing a Susceptible Population Density Among Other Explanatory Factors of African Swine Fever Spread in Wild Boar Using the Russian Federation Data, 2007-2023.
Article in Transboundary and emerging diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Detection and genetic characterization of atypical porcine pestivirus in wild boars in European Russia.Frontiers in microbiology · 2026Article
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Authors and funding
8 authors.
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Abstract
This study aims to identify the role of various natural, socioeconomic, and demographic factors in the development of the African swine fever (ASF) epizootic among wild boar in the Russian Federation (RF) from 2007 to 2023. In this study, particular emphasis was placed on testing the significance of wild boar population density as a key factor contributing to the spread of ASF within this population. During the study period, 1711 outbreaks in wild boars were reported in the RF, accounting for 41.7% of all ASF outbreaks in the country. We tested two regression approaches to model the dependance of the total number of ASF outbreaks in second-level municipal units (districts) on a range of potential explanatory factors, including the dynamically changing annual population density of wild boar. We employed negative binomial regression (NBR) and, as an alternative approach, classification and regression trees (CARTs). The predictive capabilities of both models were evaluated using 10-fold cross-validation. One of the most significant identified factors was the number of ASF outbreaks in domestic populations, which may indicate a close coexistence of both domestic and wild ASF cycles. Population density showed limited significance in the negative binomial model (
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