Abstract
Complex situations such as pandemics generally lead to consider different sources of information in the analysis. We propose a general framework for coronavirus risk assessment based on multi-criteria decision aiding (MCDA) where input variables are indicators expressed on the basis of qualitative-ordinal scales. The proposed approach, based on Sugeno Utility Functionals, makes the problem setting easy to interpret and allows us to reflect the policy-makers’ opinions on the importance of each indicator or subset of indicators. Interestingly, our approach is related to if-then rule-based systems adopted by some Governments for pandemic risk assessment and restriction policy planning.
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Anzilli, L., Cardin, M. (2022). Sugeno Integral Based Pandemic Risk Assessment. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1601. Springer, Cham. https://doi.org/10.1007/978-3-031-08971-8_4
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DOI: https://doi.org/10.1007/978-3-031-08971-8_4
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