Ito Satoshi, Bosch Jaime, Aguilar-Vega Cecilia, Isoda Norikazu, Sánchez-Vizcaíno José Manuel, Sueyoshi Masuo
South Kyushu Livestock Veterinary Medicine Center, Kagoshima University, Soo, Japan.
VISAVET Health Surveillance Center, Complutense University of Madrid, Madrid, Spain.
Transbound Emerg Dis. 2025 Apr 26;2025:1576080. doi: 10.1155/tbed/1576080. eCollection 2025.
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.
由于信息有限，野生动物传染病的控制通常被认为具有挑战性。野生动物中的一些传染病也会影响家畜，给畜牧业带来重大问题。在日本，2018年9月经典猪瘟（CSF）再次出现。鉴于有商业疫苗可用，控制措施主要包括对家猪进行疫苗接种以及向野猪分发口服疫苗。尽管做出了这些努力，但该疾病仍在继续传播，主要原因是野猪。日本具有挑战性的地理环境（约66%为森林覆盖）进一步加剧了这种传播，使得许多地区难以进入，导致监测出现空间偏差。因此，疫情形势无法得到全面了解，限制了控制措施的有效性。本研究使用纳入地理偏差校正的物种分布模型（SDM）估计野猪分布。利用爱知县的野猪观测数据开发了两个最大熵（MaxEnt）模型——一个标准模型和一个经报告偏差校正的模型。两个模型都显示出出色的预测准确性（曲线下面积[AUC]分别为0.946和0.946，灵敏度分别为0.868和0.943，特异性分别为0.999和0.991），最具影响力的变量识别顺序相似（11月的太阳辐射，其次是海拔、最湿润季度的降水量和8月的太阳辐射）。虽然两个模型都在东部识别出了高概率区域，但经偏差校正的模型还揭示了东北部扩大的高概率区域。在疫情阶段，保护农场是首要任务；然而，在根除阶段，控制措施还必须针对森林地区的野猪栖息地。通过使用开放获取的环境数据，这种建模方法可以应用于其他地区。准确估计野猪分布有助于改善野生动物疾病监测并优化口服疫苗投放策略。
