Assessing the Risk of African Swine Fever Introduction into the European Union by Wild Boar

Torre, Ana de la; Bosch, Jaime; Iglesias, I.; Muñoz, Marı́a Jesús; Mur, Lina; Martínez‐López, Beatriz; Martínez, Marta; Sánchez-Vizcaíno, José Manuel

doi:10.1111/tbed.12129

Cited by 94 publications

(100 citation statements)

References 25 publications

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“…In the Baltic countries and in Poland, it was observed that wild boar habitat suitability, neighbouring distance from infected wild boar and domestic pigs were the main risk factors for the introduction into EU free areas through infected wild boar [39]. Different studies highlighted the continuity of the geographical distribution of wild boar, wild boar management (hunting system, winter feeding), local density and size of the infected population, direct contact with dead infected wild boar, as the most relevant risks for the spread and the persistence of the virus in wild boar populations [4, 25, 40].…”

Section: Reviewmentioning

confidence: 99%

Preventive measures aimed at minimizing the risk of African swine fever virus spread in pig farming systems

Rutili²,

2016

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African swine fever (ASF) is one of the most severe diseases of pigs; it has a drastic impact on the pig industry, causing serious socio-economic consequences to pig farmers and pork producers. In Europe, there are currently two main clusters of infection; one in Sardinia caused by strains of African swine fever virus (ASFV) belonging to genotype I and another in Eastern Europe caused by strains of ASFV belonging to genotype II. The latter is inducing an acute form of ASF and it represents a serious threat to the pig sector. ASF is a disease for which there is no effective vaccine; therefore, prevention has a pivotal role in the control strategy of the disease. This review describes the main preventive measures to adopt to mitigate the risk of ASF spread in pig farming systems.

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Section: Reviewmentioning

confidence: 99%

Preventive measures aimed at minimizing the risk of African swine fever virus spread in pig farming systems

Rutili²,

2016

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“…In the case of the Iberian Peninsula, Bosch et al (2012) have recently created a habitat suitability map for wild boar based on the availability of vegetation resources, i.e., food resources and shelter. These authors used different studies to assess the risk of introducing disease along vegetation corridors crossing national borders (De la Torre et al, 2013). Furthermore, these studies also have been applied in Spanish epidemiological surveillance programs for certain diseases (Ministerio de Agricultura, Alimentación y Medio Ambiente, Spain, 2013).…”

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confidence: 99%

A maximum entropy model for predicting wild boar distribution in Spain

Bosch

Mardones

Pérez

et al. 2014

Span. j. agric. res.

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Wild boar (Sus scrofa) populations in many areas of the Palearctic including the Iberian Peninsula have grown continuously over the last century. This increase has led to numerous different types of conflicts due to the damage these mammals can cause to agriculture, the problems they create in the conservation of natural areas, and the threat they pose to animal health. In the context of both wildlife management and the design of health programs for disease control, it is essential to know how wild boar are distributed on a large spatial scale. Given that the quantifying of the distribution of wild species using census techniques is virtually impossible in the case of large-scale studies, modeling techniques have thus to be used instead to estimate animals' distributions, densities, and abundances. In this study, the potential distribution of wild boar in Spain was predicted by integrating data of presence and environmental variables into a MaxEnt approach. We built and tested models using 100 bootstrapped replicates. For each replicate or simulation, presence data was divided into two subsets that were used for model fitting (60% of the data) and cross-validation (40% of the data). The final model was found to be accurate with an area under the receiver operating characteristic curve (AUC) value of 0.79. Six explanatory variables for predicting wild boar distribution were identified on the basis of the percentage of their contribution to the model. The model exhibited a high degree of predictive accuracy, which has been confirmed by its agreement with satellite images and field surveys.Additional key words: Sus scrofa; environmental suitability; MaxEnt; spatial distribution; wildlife management; geographic information. This work has one supplementary table and three supplementary figures that do not appear in the printed article but that accompany the paper online.Abbreviations used: AUC (area under the receiver operating characteristic curve); GAM (generalised additive models); GBIF (global biodiversity information facility); GIMMS (global inventory modeling and mapping studies); GLM (generalized linear model); GRASS (geographic resources analysis support system); MaxEnt (maximum entropy); NDVI (normalized difference vegetation index); ROC (receiver operating characteristic); SDM (species distribution model); SRTM (shuttle radar topography mission); VCF (vegetation continuous fields); VIF (variance inflated factor).

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“…The risk of introduction of ASFV by wild boar into the European Union as estimated after the outbreaks in the Caucasus region and Russian Federation concerns mostly Finland, Romania, Latvia and Poland and not Greece for the moment (De la Torre et al 2013). Although ASF is not a zoonotic disease, surveillance to timely identify isolates and prevent spreading of the disease is of great importance given the absence of an effective vaccine available or any effective treatment.…”

Section: Discussionmentioning

confidence: 99%

Exposure of extensively farmed wild boars (Sus scrofa scrofa) to selected pig pathogens in Greece

Marinou

Papatsiros

Gkotsopoulos

et al. 2015

Veterinary Quarterly

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Background: Increased density and distribution of wild boar populations are likely to promote interactions and transmission of certain pathogens, not only among wild boar but also from wild boar to livestock or humans and vice versa. Objective: The purpose of this study was to determine seroprevalence against seven selected pathogens in wild boar living in four different areas in Greece. Animals and methods: In total, 359 serum samples were collected from extensively farmed wild boar (Sus scrofa scrofa) originating from four distinct geographical areas throughout Greece from April 2012 to August 2013. Samples were tested for antibodies to Actinobacillus pleuropneumoniae, African swine fever virus (ASFV), Aujeszky's disease virus (ADV), classical swine fever virus (CSFV), Erysipelothrix rhusiopathiae, Mycoplasma hyopneumoniae and porcine reproductive and respiratory syndrome virus (PRRSV). Prevalence was compared among the four regions using Fisher's exact test. Results: Low overall seropositivities of 2.4% and 5.6% were detected for E. rhusiopathiae and PRRSV, respectively, higher ones for ADV (32.0%) and the highest (72.5% and 90.5%) for M. hyopneumoniae and A. pleuropneumoniae, respectively. All sera tested were found negative for antibodies directed against CSFV and ASFV. Conclusions: This is the first report of exposure of wild boars to selected pig pathogens in Greece. These results are indicative of the circulation of these pathogens in Greece with the exception of CSFV and ASFV and suggestive of the potential role of wild boars on their maintenance and transmission to their domestic counterparts and vice versa.

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Assessing the Risk of African Swine Fever Introduction into the European Union by Wild Boar

Cited by 94 publications

References 25 publications

Preventive measures aimed at minimizing the risk of African swine fever virus spread in pig farming systems

Preventive measures aimed at minimizing the risk of African swine fever virus spread in pig farming systems

A maximum entropy model for predicting wild boar distribution in Spain

Exposure of extensively farmed wild boars (Sus scrofa scrofa) to selected pig pathogens in Greece

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