Modular framework to assess the risk of African swine fever virus entry into the European Union

BACKGROUND The recent occurrence and spread of African swine fever (ASF) in Eastern Europe is perceived as a serious risk for the pig industry in the European Union (EU). In order to estimate the potential risk of ASF virus (ASFV) entering the EU, several pathways of introduction were previously assessed separately. The present work aimed to integrate five of these assessments (legal imports of pigs, legal imports of products, illegal imports of products, fomites associated with transport and wild boar movements) into a modular tool that facilitates the visualization and comprehension of the relative risk of ASFV introduction into the EU by each analyzed pathway. RESULTS The framework's results indicate that 48% of EU countries are at relatively high risk (risk score 4 or 5 out of 5) for ASFV entry for at least one analyzed pathway. Four of these countries obtained the maximum risk score for one pathway: Bulgaria for legally imported products during the high risk period (HRP); Finland for wild boar; Slovenia and Sweden for legally imported pigs during the HRP. Distribution of risk considerably differed from one pathway to another; for some pathways, the risk was concentrated in a few countries (e.g., transport fomites), whereas other pathways incurred a high risk for 4 or 5 countries (legal pigs, illegal imports and wild boar). CONCLUSIONS The modular framework, developed to estimate the risk of ASFV entry into the EU, is available in a public domain, and is a transparent, easy-to-interpret tool that can be updated and adapted if required. The model's results determine the EU countries at higher risk for each ASFV introduction route, and provide a useful basis to develop a global coordinated program to improve ASFV prevention in the EU.

Risk of African swine fever introduction into the European Union through transport-associated routes: returning trucks and waste from international ships and planes

BACKGROUND The uncontrolled presence of African swine fever (ASF) in Russian Federation (RF) poses a serious risk to the whole European Union (EU) pig industry. Although trade of pigs and their products is banned since the official notification in June 2007, the potential introduction of ASF virus (ASFV) may occur by other routes, which are very frequent in ASF, and more difficult to control, such as contaminated waste or infected vehicles. This study was intended to estimate the risk of ASFV introduction into the EU through three types of transport routes: returning trucks, waste from international ships and waste from international planes, which will be referred here as transport-associated routes (TAR). Since no detailed and official information was available for these routes, a semi-quantitative model based on the weighted combination of risk factors was developed to estimate the risk of ASFV introduction by TAR. Relative weights for combination of different risk factors as well as validation of the model results were obtained by an expert opinion elicitation. RESULTS Model results indicate that the relative risk for ASFV introduction through TAR in most of the EU countries (16) is low, although some countries, specifically Poland and Lithuania, concentrate high levels of risk, the returning trucks route being the analyzed TAR that currently poses the highest risk for ASFV introduction into the EU. The spatial distribution of the risk of ASFV introduction varies importantly between the analyzed introduction routes. Results also highlight the need to increase the awareness and precautions for ASF prevention, particularly ensuring truck disinfection, to minimize the potential risk of entrance into the EU. CONCLUSIONS This study presents the first assessment of ASF introduction into the EU through TAR. The innovative model developed here could be used in data scarce situations for estimating the relative risk associated to each EU country. This simple methodology provides a rapid and easy to interpret results on risk that may be used for a target and cost-effective allocation of resources to prevent disease introduction.

Risk premiumin the global creditMarkets: 2006-2012

¿What have we learnt from the 2006-2012 crisis, including events such as the subprime crisis, the bankruptcy of Lehman Brothers or the European sovereign debt crisis, among others? It is usually assumed that in firms that have a CDS quotation, this CDS is the key factor in establishing the credit premiumrisk for a new financial asset. Thus, the CDS is a key element for any investor in taking relative value opportunities across a firm’s capital structure. In the first chapter we study the most relevant aspects of the microstructure of the CDS market in terms of pricing, to have a clear idea of how this market works. We consider that such an analysis is a necessary point for establishing a solid base for the rest of the chapters in order to carry out the different empirical studies we perform. In its document “Basel III: A global regulatory framework for more resilient banks and banking systems”, Basel sets the requirement of a capital charge for credit valuation adjustment (CVA) risk in the trading book and its methodology for the computation for the capital requirement. This regulatory requirement has added extra pressure for in-depth knowledge of the CDS market and this motivates the analysis performed in this thesis. The problem arises in estimating of the credit risk premium for those counterparties without a directly quoted CDS in the market. How can we estimate the credit spread for an issuer without CDS? In addition to this, given the high volatility period in the credit market in the last few years and, in particular, after the default of Lehman Brothers on 15 September 2008, we observe the presence of big outliers in the distribution of credit spread in the different combinations of rating, industry and region. After an exhaustive analysis of the results from the different models studied, we have reached the following conclusions. It is clear that hierarchical regression models fit the data much better than those of non-hierarchical regression. Furthermore,we generally prefer the median model (50%-quantile regression) to the mean model (standard OLS regression) due to its robustness when assigning the price to a new credit asset without spread,minimizing the “inversion problem”. Finally, an additional fundamental reason to prefer the median model is the typical "right skewness" distribution of CDS spreads...