DONDENA Seminar - Eugenio Valdano

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“Beyond more data: new theory for epidemic risk in structured populations” 

SPEAKER: Eugenio Valdano (INSERM)

ABSTRACT: Public health response to infectious disease epidemics aims to deliver the right intervention to the right population at the right time. Yet the drive for increasingly specific predictions has often translated into demand for ever more granular data and computationally intensive models. This feedback loop is difficult to sustain: access to data is unequal, privacy concerns are growing, achievable resolution is reaching practical limits, and highly data-dependent models may generalize poorly across settings. In this talk, I argue that greater specificity does not necessarily require more data. New theoretical and methodological approaches can identify the dimensions of population structure that matter for transmission, use existing data more effectively, and extract actionable public health information from aggregated or limited observations. Applications to respiratory infections, HIV and other sexually transmitted infections show how this perspective can improve spatially resolved risk assessment, correct misleading indicators derived from surveillance, and challenge the assumption that more targeted interventions are always more effective. Together, these examples outline a path towards epidemic models that are specific enough to inform policy while remaining interpretable, transferable and feasible across data-rich and data-limited settings.

BIO: Eugenio Valdano is a tenured researcher at INSERM (French National Institute of Health and Medical Research) and Sorbonne University in Paris, France. He is a member of the Sorbonne Public Health Institute and the Sorbonne University Modeling Outbreaks Center. Trained in theoretical physics and epidemiology, he develops data-driven mathematical models to study infectious-disease spread, focusing on human mobility, social mixing, and epidemic control.