The project examines recurrent and seasonal infectious diseases, using norovirus as a case study to explore how diagnostic supply chains can be made more resilient. The 2024–2025 Norovirus season began significantly earlier than expected, placing acute strain on NHS laboratories, community diagnostic hubs and food-safety testing services. Much existing supply chain resilience research has centred on large, high-impact disruptions such as COVID-19. While this work has yielded important insights, it focused on emergency pandemic conditions rather than on recurrent, seasonal pressures such as Norovirus. As a result, we still have a limited understanding of diagnostics as a distinct supply chain category with unique regulatory, market and operational dependencies. This gap is reinforced by the tendency of the literature to adopt either an engineering view of resilience, focused on performance, control and recovery metrics; or a socio-ecological view emphasising governance, collaboration and learning, with few studies integrating these complementary perspectives.
This project addresses these gaps by examining the structure and behaviour of the Norovirus diagnostic supply chain across human health and food-safety settings. Using a mixed-methods design, it will map diagnostic pathways, analyse NHS procurement, inventory and testing data, and develop qualitative case studies with laboratory managers, diagnostic suppliers and healthcare staff. Insights from these empirical stages will inform a system dynamics simulation model to explore how alternative supply chain structure and strategies influence system-wide resilience under different seasonal scenarios. By combining engineering and socio-ecological perspectives within a unified analytical and modelling framework, the study will generate deeper understanding of how diagnostic supply chains absorb, adapt to and transform in seasonal and unpredictable surges. The findings will offer evidence-based recommendations for improving preparedness and continuity of supply, contributing to more resilient diagnostic systems capable of responding to epidemiological changes.