An Original Network Science Framework for Modelling Medical Equipment Failure Propagation in Nigerian Hospital Systems

Medical equipment failures in Nigerian hospitals rarely occur in isolation but rather trigger cascading service disruptions as clinical workflows adapt to failed device availability, yet no theoretical framework exists for modelling the propagation of equipment failure effects through the interconnected clinical service networks of Nigerian hospitals. This dissertation develops an original network science framework for modelling medical equipment failure propagation in Nigerian hospital systems, providing theoretical tools for clinical engineering planning that minimize the systemic clinical impact of equipment failures. The theoretical framework draws on complex network theory, cascading failure theory from power systems engineering, and healthcare operations research to construct a clinical service network model in which nodes represent clinical services and equipment categories, and directed edges represent service dependency relationships. Original theoretical contributions include a failure propagation operator theory specifying how equipment failure in one network node alters the failure probability and service capacity reduction consequences at dependent downstream nodes, accounting for clinical workaround capacity, redundancy configurations, and staff adaptive behaviour. A resilience metric is derived from the framework that quantifies the robustness of a hospital equipment configuration to cascading service failures and can be optimised over equipment investment decisions. The empirical foundation is built from clinical service dependency mapping exercises conducted at four Nigerian hospitals in Lagos, Enugu, Plateau, and Kebbi states, involving structured workflow mapping interviews with clinical staff and equipment managers. The framework is implemented as a simulation model and validated against historical equipment failure impact records at two hospitals. Sensitivity analysis reveals that equipment redundancy in the three highest betweenness centrality equipment categories reduces expected service failure cascade size by forty-six percent. Keywords: network science, equipment failure propagation, hospital systems, cascading failures, clinical engineering.