A Fuzzy Logic Approach for Pandemic Risk Assessment and Restrictions Design

In this study we propose a novel approach for coronavirus risk assessment and containment measures policies, based on multi-criteria decision analysis (MCDA). Governments’ coronavirus policies typically determine the intensity of social and economic restrictions for specific areas (e.g. school closures) based on sharp classifications of risk, resulting from a dashboard of core indicators. This approach is likely to produce violations of fundamental equity and fairness principles, e.g., assigning very different restrictions to areas which only exhibit small differences in the core indicators. We propose to mitigate such distortions by introducing a general model of health-risk assessment, based on MCDA with fuzzy thresholds that allow for a smooth transition of restrictions between risk classes. Our general framework can accommodate numerous suitable aggregation operators and weighting schemes, hence providing a flexible tool to support policymakers in their strategic decisional process.