EpiControl: a data-driven tool for optimising epidemic interventions and automating scenario planning to support real-time response

Deciding among control policies during outbreaks is challenging due to unreliable data and uncertain intervention outcomes. Complex disease models can simulate economic and health outcomes, but intensive data or computational demands and the specialist knowledge needed for calibration limits their real-time application. Simpler, faster and more generalised tools exist but typically only estimate or forecast transmission dynamics under preset assumptions. We present EpiControl, a flexible decision-support tool (in R) that fits semi-mechanistic models to routine data and leverages feedback-control and model-based learning to balance complex policy-simulation with real-time responsiveness. EpiControl generates intervention scenarios that automatically evolve with unfolding dynamics, minimise costs, meet user-defined policy targets (e.g., epidemic peak control) and remain robust to unanticipated changes. Using Ebola virus and COVID-19 case studies, we establish how EpiControl discovers optimal intervention policies that prevent hospital overload, reduce societal disruption and retain control despite uncertain immunity, vaccination and variant dynamics.