Landscape elements affecting the ingestion of oral vaccine against classical swine fever in wild boar

Endo, Tomohiko; Hirata, Shigeki; Hayama, Yoko; Kodera, Yuuji; Takeuchi, Masahiko

doi:10.1007/s10344-023-01733-8

Cited by 1 publication

(2 citation statements)

References 38 publications

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“…According to the previous study, wild boar are less likely to appear in urban areas with high road density and high human activity 52 . Rather, wild boars tend to appear in areas where road density is not very dense and human activity is not frequent, i.e., areas with diverse landscapes are highly important, such as at the boundary between mountainous areas and human settlements, where they are more likely to forage for food 53 .…”

Section: Discussionmentioning

confidence: 99%

“…Ikeda et al 57 identified factors influencing wild boar vaccine uptake rates from biological, environmental, and geographical perspectives using spatial Bayesian generalized linear model. Endo et al 53 used a generalized linear mixed model to investigate landscape factors correlated with vaccine uptake. These studies analyzed causal relationships modeled by regression analysis.…”

Section: Discussionmentioning

confidence: 99%

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Application of machine learning with large-scale data for an effective vaccination against classical swine fever for wild boar in Japan

Ito,

Aguilar-Vega,

Bosch

et al. 2024

Sci Rep

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Classical swine fever has been spreading across the country since its re-emergence in Japan in 2018. Gifu Prefecture has been working diligently to control the disease through the oral vaccine dissemination targeting wild boars. Although vaccines were sprayed at 14,000 locations between 2019 and 2020, vaccine ingestion by wild boars was only confirmed at 30% of the locations. Here, we predicted the vaccine ingestion rate at each point by Random Forest modeling based on vaccine dissemination data and created prediction surfaces for the probability of vaccine ingestion by wild boar using spatial interpolation techniques. Consequently, the distance from the vaccination point to the water source was the most important variable, followed by elevation, season, road density, and slope. The area under the curve, model accuracy, sensitivity, and specificity for model evaluation were 0.760, 0.678, 0.661, and 0.685, respectively. Areas with high probability of wild boar vaccination were predicted in northern, eastern, and western part of Gifu. Leave-One-Out Cross Validation results showed that Kriging approach was more accurate than the Inverse distance weighting method. We emphasize that effective vaccination strategies based on epidemiological data are essential for disease control and that our proposed tool is also applicable for other wildlife diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%