The presence of African swine fever (ASF) in the Caucasus region and Russian Federation has increased concerns that wild boars may introduce the ASF virus into the European Union (EU). This study describes a semi-quantitative approach for evaluating the risk of ASF introduction into the EU by wild boar movements based on the following risk estimators: the susceptible population of (1) wild boars and (2) domestic pigs in the country of origin; the outbreak density in (3) wild boars and (4) domestic pigs in the countries of origin, the (5) suitable habitat for wild boars along the EU border; and the distance between the EU border and the nearest ASF outbreak in (6) wild boars or (7) domestic pigs. Sensitivity analysis was performed to identify the most influential risk estimators. The highest risk was found to be concentrated in Finland, Romania, Latvia and Poland, and wild boar habitat and outbreak density were the two most important risk estimators. Animal health authorities in at-risk countries should be aware of these risk estimators and should communicate closely with wild boar hunters and pig farmers to rapidly detect and control ASF.

Despite efforts to prevent the appearance and spread of African swine fever (ASF) in the European Union, several Member States are now affected (Lithuania, Poland, Latvia and Estonia). Disease appearance in 2014 was associated with multiple entrances linked to wild boar movement from endemic areas (EFSA Journal, 8, 2015, 1556), but the risk of new introductions remains high (Gallardo et al., Porcine Health Management, 1, and 21) as ASF continues to be active in endemic countries (Russian Federation, Belarus and Ukraine). Since 2014, the number of ASF notifications has increased substantially, particularly in wild boar (WB), in parallel with slow but constant geographical advance of the disease. This situation suggests a real risk of further disease spread into other Member States, posing a great threat to pig production in the EU. Following the principles of the risk-based veterinary surveillance, this article applies a methodology developed by De la Torre et al. (Transboundary and Emerging Diseases, 62, and 272) to assess the relative risk of new introductions of ASF by natural movements of WB according to the current epidemiological situation. This update incorporates the most recent available data and an improved version of the most important risk estimator: an optimized cartographic tool of WB distribution to analyse wild boar suitable habitat. The highest relative risk values were estimated for Slovakia (5) and Romania (5), followed by Finland (4), Czech Republic (3) and Germany (3). Relative risk for Romania and Finland is associated mainly with disease entrance from endemic areas such as the Russian Federation and Ukraine, where the disease is currently spreading; relative risk for Germany and Czech Republic is associated mainly with the potential progress of the disease through the EU, and relative risk for Slovakia is associated with both pathways. WB habitat is the most important risk estimator, whereas WB density is the least significant, suggesting that WB presence is more relevant than density. These results can provide actionable advice for dealing with risk. They can be directly used to inform risk-based national strategies and identify countries that may need to pay greater attention to surveillance or conduct additional evaluations at the subnational level.

The current African swine fever (ASF) epidemic in Eurasia represents a risk for the swine industry with devastating socio-economic and political consequences. Wild boar appears to be a key factor in maintaining the disease in endemic areas (mainly the Russian Federation) and spreading the disease across borders, including within the European Union. To help predict and interpret the dynamics of ASF infection, we developed a standardized distribution map based on global land cover vegetation (GLOBCOVER) that quantifies the quality of available habitats (QAH) for wild boar across Eurasia as an indirect index for quantifying numbers of wild boar. QAHs were estimated using a seven-level scale based on expert opinion and found to correlate closely with georeferenced presence of wild boar (n = 22 362): the highest wild boar densities (74.47%) were found in areas at the two highest QAH levels, while the lowest densities (5.66%) were found in areas at the lowest QAH levels. Mapping notifications from 2007 to 2016 onto the QAH map showed that in endemic areas, 60% of ASF notifications occurred in domestic pigs, mostly in agricultural landscapes (QAHs 1.75 and 1) containing low-biosecurity domestic pig farms. In the EU, in contrast, 95% of ASF notifications occurred in wild boar, within natural landscapes (QAH 2). These results suggest that the QAH map can be a useful epi-tool for defining risk scenarios and identifying potential travel corridors for ASF. This tool will help inform resource allocation decisions and improve prevention, control and surveillance of ASF and potentially of other diseases affecting swine and wild boar in Eurasia.

No abstract

Since September 2018, nearly 900 notifications of classical swine fever (CSF) have been reported in Gifu Prefecture (Japan) affecting domestic pig and wild boar by the end of August 2019. To determine the epidemiological characteristics of its spread, a spatio-temporal analysis was performed using actual field data on the current epidemic. The spatial study, based on standard deviational ellipses of official CSF notifications, showed that the disease likely spread to the northeast part of the prefecture. A maximum significant spatial association estimated between CSF notifications was 23 km by the multi-distance spatial cluster analysis. A space-time permutation analysis identified two significant clusters with an approximate radius of 12 and 20 km and 124 and 98 days of duration, respectively. When the area of the identified clusters was overlaid on a map of habitat quality, approximately 82% and 75% of CSF notifications, respectively, were found in areas with potential contact between pigs and wild boar. The obtained results provide information on the current CSF epidemic, which is mainly driven by wild boar cases with sporadic outbreaks on domestic pig farms. These findings will help implement control measures in Gifu Prefecture. 108 domestic pig (DP) (square) and wild boar (WB) (circle). Ellipses with centroids were combined to indicate the 109 directional trend of the CSF outbreaks.110 2.2. Multi-Distance Spatial Cluster Analysis 111The multi-distance spatial cluster analysis was applied to explore the maximum distance 112 between cases of CSF notifications. The results indicated that 23 km was the maximum distance of 113 the significant spatial association between CSF notifications in Gifu Prefecture. The obtained 114 maximum distance was used in the subsequent analyses. 115 2.3. Kernel Density Estimation Analysis 116The kernel density estimation analysis was applied to describe the spatial distribution of the CSF 117 notifications. The analysis showed that the highest density of CSF notifications was located in the 118 southern part of Gifu Prefecture (Figure 2) with further expansion to the east. Among the 16 CSF-domestic farms were located in areas with very low density of notifications (80%), followed by areas 122 with low density (20%). The analysis revealed that CSF-positive farms were located in areas with 123 higher density of notifications, whereas the non-affected farms tended to locate in areas with low 124 density. 125 126 Figure 2. Density of CSF notifications in Gifu Prefecture.127 The heat map illustrates the estimated kernel density of CSF notifications (notifications/km 2 ) from very high 128 (red) to very low (transparent). Each coloured area indicates the density of CSF notifications per square 129 kilometer: very high (> 0.400), high (0.300-0.399), medium (0.200-0.299), low (0.100-0.199), and very low (< 130 0.100). The highest density of CSF notifications was located in the southern part of Gifu Prefecture. A very low 131 density of CSF notifications was located in other areas of the pr...

BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

To predict the risk of incursion of Crimean-Congo haemorrhagic fever virus (CCHFV) in livestock in Europe introduced through immature Hyalomma marginatum ticks on migratory birds under current conditions and in the decade 2075-2084 under a climate-change scenario. A spatial risk map of Europe comprising 14 282 grid cells (25 × 25 km) was constructed using three data sources: (i) ranges and abundances of four species of bird which migrate from sub-Saharan Africa to Europe each spring, namely Willow warbler (Phylloscopus trochilus), Northern wheatear (Oenanthe oenanthe), Tree pipit (Anthus trivialis) and Common quail (Coturnix coturnix); (ii) UK Met Office HadRM3 spring temperatures for prediction of moulting success of immature H. marginatum ticks and (iii) livestock densities. On average, the number of grid cells in Europe predicted to have at least one CCHFV incursion in livestock in spring was 1·04 per year for the decade 2005-2014 and 1·03 per year for the decade 2075-2084. In general with the assumed climate-change scenario, the risk increased in northern Europe but decreased in central and southern Europe, although there is considerable local variation in the trends. The absolute risk of incursion of CCHFV in livestock through ticks introduced by four abundant species of migratory bird (totalling 120 million individual birds) is very low. Climate change has opposing effects, increasing the success of the moult of the nymphal ticks into adults but decreasing the projected abundance of birds by 34% in this model. For Europe, climate change is not predicted to increase the overall risk of incursion of CCHFV in livestock through infected ticks introduced by these four migratory bird species

No abstract