Summary 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.

African swine fever (ASF) is a notifiable, highly contagious and fatal viral disease of both wild and domestic suids. ASF has a severe socio-economic impact on the meat industry globally (Beltrán-Alcrudo,

Anthrax is a zoonotic disease caused by a spore-forming gram-positive bacterium, Bacillus anthracis (BA), in which soil is the primary reservoir. The geographic distribution of the disease appears to be restricted by a combination of climatic and environmental conditions. Among the top five zoonotic diseases, Anthrax is the second priority zoonosis in Ethiopia. Increased anthropogenic factors inside wildlife protected areas may worsen the spillover of the disease from domestic animals to wildlife. Consequently, the prediction of the environmental suitability of BA spores to locate a high-risk area is urgent. Here we identified a potentially suitable habitat for BA spores survival and a high-risk area for appropriate control measures. Our result revealed that a relatively largest segment of Omo National Park located on the western side and more than half of the total area of Mago National Park bordering Hamer, Bena Tsamay, and south Ari were categorized under a high-risk area for the anthrax occurrence in the current situation. Therefore, the findings of this study provide the priority area to focus and allocating resources for effective surveillance, prevention, and control of anthrax before it cause devastating effect on wildlife.

African swine fever (ASF) free China has experienced a sudden multi-focal and multi-round of 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 potential Trans-Siberian transmission path and its associated ASF arrival dates. Least Cost Paths (LCPs) between Eastern Europe and NE China were plotted. 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 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.

The spread of PPR is an active area of research and under close scrutiny of the national government. There has not been significant progress on this issue yet in China, among others, because of a lack of a suitable monitoring method and consequently of information.In the published paper, the presented MaxEnt model seems neither a good geographic predictor of PPR risk nor useful for PPR control. In the concluding coloured map, the reader can easily assess that not all occurrence points are covered by the predicted probability of PPR occurrence ('risk') in China. Many PPR presence points are spread over the full width of the northern half of China in the low-risk zone (blue). Further, the presented model fails to predict the historical PPR presence in Tibet from 2007.Next, we tried to assess the input data quality and the application of the MaxEnt algorithm. Several issues were identified. The most important issue could be that the presence point data do not represent reported PPR point locations, as suggested in Methods section. Instead, each presence point seems to represent a county 1 as described in the last text line of the publication. However, the reader is not informed about how the point is located within each county. Secondly, Methods section informs us that the presence points are 'rarefied at 1 km 2 '. If we assume that the PPR presence points are located in the centre of counties, none of the points might be within a 1-km distance of the centre of the neighbouring county.However, the reader is told that the rarefication has eliminated 15 out of 294 presence points. Further, assuming point locations within the centre of counties, the 1-km distance is too small to achieve substantial rarefication and consequently spatial auto-correlation of presence points has not been reduced. This illustrates that it was improper to accept a standard (1 km 2 ) from another related study to reduce spatial auto-correlation. The prediction is therefore doubtful, because the use of uncorrected spatial auto-correlated input data. The use of the Pearson correlation coefficient for elimination of environmental variables needs clarification as well. Are both variables of a pair of correlated variables eliminated? If so, the pairing procedure needs to be made transparent. The Pearson statistic assumes a normal distribution and a linear correlation. However, the presented results suggest that precipitation is not linearly correlated with PPR virus presence. Other approaches, including PCA and stepwise backward elimination, are proven ways to effectively and transparently reduce the number of environmental variables (Farrell et al., 2019; Wubetie, 2019). Finally, the use of goat and sheep density as environmental predictor variables of risk areas seems doubtful. More appropriate would be to 'cross/overlay' the risk areas obtained by the MaxEnt model based on bioclimatic and ultraviolet variables with the goat and sheep density maps.Based on our above assessment, a scientifically sound PPR risk zoning for China needs multiple addi...