Could wild boar be the Trans-Siberian transmitter of African swine fever?

China has experienced a sudden multi-focal and multi-round of African swine fever (ASF) outbreaks during 2018. The subsequent epidemiological survey resulted in a debate including the possibility of a transboundary spread from European Russia to China through wild boar. We contribute to the debate by assessing a hypothetical overland Euro-Siberian transmission path and its associated ASF arrival dates. We selected the maximum entropy algorithm for spatial modelling of ASF-infected wild boar and the Spatial Distribution Modeller in ArcGIS to plot Least Cost Paths (LCPs) between Eastern Europe and NE China. The arrival dates of ASF-infected wild boar have been predicted by cumulative maximum transmission distances per season and cover with their associated minimum time intervals along the LCPs. Our results show high costs for wild boar to cross Kazakhstan, Xinjiang (NW China) and/or Mongolia to reach NE China. Instead, the Paths lead almost straight eastward along the 59.5° northern latitude through Siberia and would have taken a minimum of 219 or 260 days. Therefore, infected wild boar moving all the way along the LCP could not have been the source of the ASF infection in NE China on 2 August 2018.

High probability areas for ASF infection in China along the Russian and Korean borders.

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.