Leveraging Particle Physics to Predict and Prevent Chronic Health Challenges

This project applies advanced data analysis techniques, adapted from particle physics, to improve public health intelligence by identifying and modelling the social determinants of health.

Chronic health challenges, particularly non-communicable diseases (NCDs), account for 74% of global deaths. Despite their preventable nature, gaps in data integration, predictive modelling, and proactive intervention hinder effective public health strategies. This project applies advanced data analysis techniques, adapted from particle physics, to improve public health intelligence by identifying and modelling the social determinants of health. By integrating real-world datasets from Hackney Council, ONS, and additional sources, the project aims to uncover hidden patterns in health inequities and provide actionable insights for policymakers and communities.

Project Objectives:
-Develop a novel, physics-informed data analytics framework to predict chronic health risks.
-Enhance local health intelligence by integrating diverse datasets and modelling health determinants.
-Provide real-time, granular insights to inform targeted public health interventions.
-Foster interdisciplinary collaboration between particle physics, public health, urban strategy, and data science.
-Lay the groundwork for scaling this approach to broader public health systems and policy applications.