Pandemic Preparedness: AI, Data & Predictive Modelling

Session overview

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Context:

DPH2026 takes place six years after the start of the COVID-19 pandemic. Far enough to assess what has genuinely changed, and close enough that the urgency of “next time” remains real.

Lack of data for public health, their interpretability and access needs to be thought about and well planned when designing healthcare systems and data collection processes, to ensure equity, representativeness and quality are embedded. The systems must include data on hard to reach populations as as well as from those who out out form public healthcare for private options.

This session addresses the deployment gap: the distance between what digital and data technologies can do in principle and what is actually running at population scale. It focuses on two domains where this gap is particularly consequential. IoT-based surveillance (including the largely untapped potential of smartphones as ambient sensing devices) and the digitalisation of point-of-care and home testing data. Predictive and ecological modelling are also part of these domains. Panellists should be prepared to speak honestly about what works, what has failed to scale, and why.

Bringing together behaviour data, EPR and wearable devices such as Apple Watch and Fitbit is a missed opportunity. Discussing challenges with scale up from pilot to national and international systems and sustainable use of AI driven precision behaviour advice. Also, how to bring on board those currently not using the wearable devices and what implications of these missing data are for public health preparedness.

Speakers

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