RT Journal Article
T1 Application of machine learning with large-scale data for an effective vaccination against classical swine fever for wild boar in Japan
A1 Ito, Satoshi
A1 Aguilar Vega, Cecilia
A1 Bosch López, Jaime Alfonso
A1 Isoda, Norikazu
A1 Sánchez-Vizcaíno Rodríguez, José Manuel
AB 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.
PB Springer
SN 2045-2322
YR 2024
FD 2024-03-04
LK https://hdl.handle.net/20.500.14352/103352
UL https://hdl.handle.net/20.500.14352/103352
LA eng
DS Docta Complutense
RD 15 ago 2024