Can time-dependent optimal interventions reduce Salmonella spread? A multi-pathway ODE model using real data from EU

Salmonella remains a major public health concern worldwide due to its complex transmission pathways involving humans, food, the environment, and vectors, making efficient allocation of limited control resources essential for effective disease management. In this study, we develop a comprehensive compartmental ordinary differential equation (ODE) model to investigate optimal intervention strategies for reducing Salmonella transmission. The model incorporates multiple transmission routes and is calibrated using reported Salmonella case data from the EU/EEA spanning 2007–2023. Global sensitivity analysis identifies the most influential epidemiological parameters governing disease dynamics. An optimal control framework is then formulated to minimize both infection burden and intervention costs, yielding time-dependent control strategies. Five control measures were examined: environmental decontamination, food safety measures, vector control, treatment and isolation, and public health education. These strategies were tested as constant, early, delayed, and pulsed scenarios. Results indicate that coordinated, dynamic interventions substantially reduce Salmonella incidence compared to uncontrolled scenarios, with early and constant strategies showing the greatest cost-effectiveness. These findings provide a quantitative, data-informed framework to support evidence-based decision-making for more efficient Salmonella control and prevention.