PRIORIZAÇÃO DE ÁREAS DE VIGILÂNCIA, BASEADO EM MODELAGEM E RISCO EPIDEMIOLÓGICO DA PRESENÇA DE JAVALIS (Sus scrofa) NO MATO GROSSO DO SUL PARA FORTALECIMENTO DO PLANO DE CONTROLE DE PESTE SUÍNA CLÁSSICA

Abstract

Animal disease outbreaks pose risks to livestock industries around the world. The presence of wild boar potentiates these risks for the occurrence of Classical Swine Fever and African Swine Fever, transboundary diseases, targets of the Integrated Swine Disease Surveillance Plan. The objective of the work was to build a risk model to direct and strengthen actions related to the monitoring and risk of the presence of wild boar populations throughout the geographic area of the state of Mato Grosso do Sul. To identify the areas of health surveillance, where the potential risk of contact and transmission of diseases is greater, an analytical process of combination and prioritization of variables was used, applying the AHP method as the main tool, considering demographic patterns, such as the density of breeding sites, and the probability of wild boar occurrence, calculated using potential distribution models. We observed that commercial farms are distributed in different levels of priority of sanitary surveillance. Considering a neutral risk scenario, that is, the variables are equally prioritized, the results presented commercial farms positioned at a moderately high (41.2%), high priority level (33%), very high (11%) and medium (7.9%), respectively. Considering a moderate scenario, the level of priority for surveillance was medium-high for 47% of the farms and medium for 34.8% of them. The model obtained can be used as a tool for prioritizing actions by the State's Official Veterinary Service within the scope of surveillance actions. The work indicates the need to strengthen the wild boar occurrence databases in the geographic area covered by the State's Official Veterinary Service, as well as the maintenance and constant updating and expansion of other Systems with accurate geographic information of pig production establishments, technified and non-technified, to support the construction of forecasting and risk models that are increasingly robust and improved.