Evidence map›Paper›PMID 40761484›Full record

ArticleTransboundary and emerging diseases2025

Optimizing Oral Vaccine Distribution Strategies for Wild Boars Through Bias-Corrected Habitat Modeling: A Case Study of Classical Swine Fever Control in Japan.

Satoshi Ito, Jaime Bosch, Cecilia Aguilar-Vega, Norikazu Isoda, José Manuel Sánchez-Vizcaíno, Masuo Sueyoshi

Abstract read
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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. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Satoshi ItoSouth Kyushu Livestock Veterinary Medicine Center, Kagoshima University, Soo, Japan.ORCID https://orcid.org/0000-0001-9653-7520
Jaime BoschVISAVET Health Surveillance Center, Complutense University of Madrid, Madrid, Spain.ORCID https://orcid.org/0000-0002-5397-471X
Cecilia Aguilar-VegaVISAVET Health Surveillance Center, Complutense University of Madrid, Madrid, Spain.ORCID https://orcid.org/0000-0001-5981-7291
Norikazu IsodaLaboratory of Microbiology, Department of Disease Control, Faculty of Veterinary Medicine, Hokkaido University, Sapporo, Japan.ORCID https://orcid.org/0000-0003-3480-988X
José Manuel Sánchez-VizcaínoVISAVET Health Surveillance Center, Complutense University of Madrid, Madrid, Spain.
Masuo SueyoshiSouth Kyushu Livestock Veterinary Medicine Center, Kagoshima University, Soo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Control of infectious diseases in wildlife is often considered challenging due to the limited availability of information. Some infectious diseases in wildlife can also affect livestock, posing significant problems for the animal farming industry. In Japan, classical swine fever (CSF) reemerged in September 2018. Given the availability of commercial vaccines, control measures mainly involve the vaccination of domestic pigs and the distribution of oral vaccines to wild boars. Despite these efforts, the disease continues to spread, primarily due to wild boars. This transmission is further exacerbated by Japan's challenging geography-about 66% forested-making many areas difficult to access and leading to spatial bias in surveillance. As a result, the epidemic situation cannot be fully understood, limiting the effectiveness of control measures. This study estimated wild boar distribution using a species distribution model (SDM) that incorporates geographic bias correction. Two maximum entropy (MaxEnt) models-a standard model and a reporting bias-corrected model-were developed using wild boar observation data from Aichi Prefecture. Both models demonstrated excellent prediction accuracy (area under the curve [AUC] of 0.946 and 0.946, sensitivity of 0.868 and 0.943, and specificity of 0.999 and 0.991), with the most influential variables identified in a similar order (solar radiation in November, followed by elevation, precipitation during the wettest quarter, and solar radiation in August). While both models identified high-probability areas in the east, the bias-corrected model also revealed expanded high-probability zones in the northeast. During the epidemic phases, protecting farms takes priority; however, in eradication phases, control measures must also target wild boar habitats in forested areas. By using open-access environmental data, this modeling approach can be applied to other regions. Accurate estimation of wild boar distribution can contribute to improving wildlife disease surveillance and optimizing oral vaccine delivery strategies.

Indexed as

Classical Swine FeverSus scrofaVaccinationViral VaccinesAdministration, OralAnimalsClassical Swine Fever VirusEcosystemJapanSwineViral Vaccinesanimal diseaseclassical swine feverinfectious diseasespecies distribution modelsurveillancewild boarwildlife epidemiology

Identifiers

PMID40761484
PMCPMC12321435

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.