A people-centric framework for worst-case disruption analysis of interdependent infrastructure systems

Abstract

Critical infrastructure systems (CISs) sustain modern societies, yet their interdependencies allow local disruptions to cascade across systems and amplify socio-economic losses. Hazard-specific models represent physical mechanisms but often struggle to capture the full uncertainty and complexity of disruption impacts, while worst-case disruption analysis complements them by identifying upper-bound consequences under the most adverse conditions. However, existing worst-case analyses usually optimize system performance metrics and overlook a logical interdependency created by people who jointly depend on multiple CISs’ services. We propose a people-centric worst-case disruption modelling framework to identify failure scenario that leads to the largest impacts on people under both localized and non-localized disruptions, while capturing the new logical interdependency. Applied to power, gas, water and road-transport systems in a region, results reveal that worst-case impacts and single- versus multi-system outage patterns vary with disruption intensity and interdependency strength. In contrast, traditional performance-centric worst-case analyse identifies different disruption scenarios and underestimates affected populations by up to 114.65 %. Sensitivity analyses on CIS topologies and interdependencies, people-centric objective functions, and correlations in service states across zones further demonstrate how input parameters shape worst-case disruption scenarios. Together, these findings underscore the importance of integrating a people-centric perspective into worst-case disruption analyses to inform disaster risk reduction.