Abstract for: A System Dynamics Framework for Transport-Based Early Warning: Modelling Travel-Capable Infectious Population

Global air travel enables infectious diseases to spread internationally before detection, requiring management of both local transmission and importation risks. Existing early warning and wastewater-based systems integrate diverse data but focus on anomaly detection and short-term forecasts, rarely providing mechanistic prevalence estimates. Travel-coupled models similarly do not export symptom-stratified, travel-capable infectious populations as reusable outputs. We develop a System Dynamics model extending a standard SEIR framework by stratifying infectiousness into presymptomatic, asymptomatic, mildly symptomatic, and severely symptomatic compartments, and defining a travel-capable infectious population as a weighted aggregate. Using mpox in Kinshasa, Democratic Republic of the Congo, we parameterise the model with epidemiological data to generate time-varying trajectories, providing a generic formulation adaptable to other human-to-human pathogens. We demonstrate how the travel‑capable infectious population trajectories can be used as upstream inputs for air‑travel importation models and for the design of aircraft and airport wastewater surveillance, and we illustrate how key clinical and behavioural assumptions, such as asymptomatic fractions and travel capability of mildly and severely symptomatic cases, affect these trajectories. By making the travel-capable infectious population an explicit, exportable metric, the framework helps bridge region-of-endemicity transmission modelling and transport-linked early warning system design. This enables more consistent integration between epidemiological models and downstream applications such as importation risk estimation and wastewater surveillance, supporting more targeted, data-informed, and adaptive surveillance strategies under uncertainty. For editing text, grammar check and wording for clarity.