Frontiers in Veterinary Science (Sep 2019)
Standardized Risk Analysis Approach Aimed to Evaluate the Last African Swine Fever Eradication Program Performance, in Sardinia
Abstract
From more than 40 years African swine fever (ASF) is endemic in Sardinia. Historically, areas at higher risk are located throughout some inland parts of this island where domestic pigs are still illegally kept in semi-wild conditions, living in contact with the local wild boar population, thereby creating perfect conditions for disease endemicity. A new eradication plan (EP-ASF15-18) has been ongoing for the past 3 years, based on a comprehensive strategy adapted to the local situation and focused on strong actions on domestic pig farms, wild boars (WB), and the third Sardinian typical involved population [illegal free-ranging pigs (FRPs)]. A fundamental aspect of the plan is the classification of pig farms as “controlled” or “certified,” based on clinical, structural, and biosecurity characteristics. The eradication plan also provides for strong action against illegal farms and pig meat marketing channels. In addition, this plan establishes specific control measures for WB hunting and ASF checks. Each control strategy is specifically based on municipality risk level, to focus actions and resources on areas at higher risk of endemic or re-emerging ASF. Thus, precise risk classification is fundamental to this goal. The aim of the present work was to establish an ASF risk index, to provide a summary measure of the risk level in the Sardinian municipalities. This synthetic measure can express the different aspects of a multidimensional phenomenon with a single numerical value, facilitating territorial and temporal comparisons. To this end, retrospective data (years 2011–2018) were used. The ASF risk index is the result of the algorithmic combination of numerical elementary indicators: disease prevalence in the suid populations, WB compliance with EP-ASF15-18, domestic pig compliance with EP-ASF15-18, and presence of FRPs. A negative binomial regression model has been applied and predictors calculated to obtain a risk index for each municipality. The result of the risk analysis was discussed and considered according to expert opinion and consensus. The results of this study, expressed as risk score and classified into five risk levels, can be used to help define actions to be carried out in each Sardinian municipality, according to the risk assessment for the territory.
Keywords
