Abstract
African Swine Fever (ASF) is an infectious and devastating viral disease that affects pigs. Measures to control this disease may have critical socioeconomic consequences, especially for low and mediumincome countries. Therefore, the implementation of actions to control ASF may positively impact several Sustainable Development Goals, such as ending poverty, zero hunger, and responsible consumption and production. Countries that are yet to be affected by the disease suffer from a lack of historical data with which to devise policies to prevent the appearance of ASF. This work introduces a novel methodology to quantify risk factors related to ASF appearing using expert knowledge. The methodology is based on operator weight average, in which weights are determined by using linguistic labels and a consensus mechanism. The proposed approach does not require particular quantitative knowledge from the experts about how weights are determined. The methodology was used to quantify the relevance of 21 different factors and 6 categories (groups of factors) linked to the appearance of ASF in Colombia, using the opinions of 15 domain experts. The results suggest that this approach may help quantify factors and categories without relying on any historical data of occurrence, resulting in consistent quantifications similar to those reported in the literature.
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Acknowledgements
The support offered by PorkColombia-FNP, the Data Analytics Laboratory of the National University of Colombia (DataLab) and the Antonio Nariño University of Colombia, as well as the support of the project PID2020-112754GB-I00 from the Spanish Govern financed with FEDER funds are appreciated.
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Cables, E.H., Moreno, F., Lamata, M.T., Gómez, F. (2022). Quantification of the Risk of ASF Appearance Using OWA Operators. In: Verdegay, J.L., Brito, J., Cruz, C. (eds) Computational Intelligence Methodologies Applied to Sustainable Development Goals. Studies in Computational Intelligence, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-030-97344-5_6
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