Sandor Beregi (Imperial College London) – EpiControl: Model-Predictive Optimisation of Epidemic Response Policy and Interaction with Population Behaviour

When and how should we intervene to manage an emerging infectious disease most effectively? Deciding when to enforce or relax non-pharmaceutical interventions (NPIs) based on real-time outbreak surveillance data is a central challenge in infectious disease epidemiology. Practical surveillance data, often characterised by reporting delays and infection under-ascertainment, can misinform decision-making. This may lead to mistimed NPIs that fail to control disease spread or allow harmful epidemic peaks that overwhelm healthcare capacities.
In this talk, I will introduce EpiControl, a novel model-predictive control algorithm designed to optimise NPI decisions by jointly minimising cumulative future risks and costs across stochastic epidemic projections. I will demonstrate how this algorithm outperforms data-insensitive strategies while also discussing the intrinsic limitations of surveillance quality, disease growth rates, and decision frequency in flattening epidemic peaks or reducing endemic oscillations. Additionally, I will present my ongoing research on integrating population behaviour into the policy-making framework.