Center of Conservation Medicine & Ecological Safety, Northeast Forestry University, Harbin, Heilongjiang province, China.
College of Wildlife Resource, Northeast Forestry University, Harbin, Heilongjiang province, China.
Transbound Emerg Dis. 2019 Mar;66(2):852-864. doi: 10.1111/tbed.13094. Epub 2019 Jan 10.
African swine fever (ASF) is a transcontinental, contagious, fatal virus disease of pig with devastating socioeconomic impacts. Interaction between infected wild boar and domestic pig may spread the virus. The disease is spreading fast from the west of Eurasia towards ASF-free China. Consequently, prediction of the distribution of ASF along the Sino-Russian-Korean borders is urgent. Our area of interest is Northeast China. The reported ASF-locations in 11 contiguous countries from the Baltic to the Russian Federation were extracted from the archive of the World Organization for Animal Health from July 19, 2007 to March 27, 2017. The locational records of the wild boar were obtained from literature. The environmental predictor variables were downloaded from the WorldClim website. Spatial rarefication and pair-wise geographic distance comparison were applied to minimize spatial autocorrelation of presence points. Principal component analysis (PCA) was used to minimize multi-collinearity among predictor variables. We selected the maximum entropy algorithm for spatial modelling of ASF and wild boar separately, combined the wild boar prediction with the domestic pig census in a single map of suids and overlaid the ASF with the suids map. The accuracy of the models was assessed by the AUC. PCA delivered five components accounting for 95.7% of the variance. Spatial autocorrelation was shown to be insignificant for both ASF and wild boar records. The spatial models showed high mean AUC (0.92 and 0.97) combined with low standard deviations (0.003 and 0.006) for ASF and wild boar, respectively. The overlay of the ASF and suids maps suggests that a relatively short sector of the Sino-Russian border has a high probability entry point of ASF at current conditions. Two sectors of the Sino-Korean border present an elevated risk.
非洲猪瘟（ASF）是一种跨大陆、传染性、致命的猪病毒病，对社会经济具有破坏性影响。受感染的野猪和家猪之间的相互作用可能会传播病毒。该疾病正在从欧亚大陆西部迅速传播到无 ASF 的中国。因此，迫切需要预测 ASF 在中国中俄朝边境的分布情况。我们的兴趣区域是中国东北地区。从 2007 年 7 月 19 日到 2017 年 3 月 27 日，从世界动物卫生组织档案中提取了来自波罗的海到俄罗斯联邦的 11 个毗邻国家的 ASF 报告地点。野猪的位置记录从文献中获得。环境预测变量从 WorldClim 网站下载。进行空间稀疏化和成对地理距离比较，以最小化存在点的空间自相关。主成分分析（PCA）用于最小化预测变量之间的多重共线性。我们分别选择最大熵算法对 ASF 和野猪进行空间建模，将野猪预测与家猪普查相结合，在一张野猪地图上叠加 ASF。通过 AUC 评估模型的准确性。PCA 提供了五个成分，占方差的 95.7%。ASF 和野猪记录均显示空间自相关不显著。空间模型显示 ASF 和野猪的平均 AUC 均较高（分别为 0.92 和 0.97），标准差较低（分别为 0.003 和 0.006）。ASF 和野猪地图的叠加表明，在当前条件下，中俄边境的一个相对较短的区域具有较高的 ASF 传入概率。中朝边境的两个区域存在较高的风险。
