The purpose of this program is to enable all people to live long, productive lives by eliminating the modifiable drivers of the world’s greatest health burdens. We are building and deploying a novel prevention service delivery platform that proactively identifies individuals most at risk of disease and engages them with innovative interventions to manage risk factors via an infrastructure-lite, digitally enabled and community-based care model.    

Interconnection between health, longevity and the economy

Project Examples:

• Exploring the impact of disease prevention strategies on economic growth and government budgets. By integrating clinical health microdata with macroeconomic modelling, this research will study how reducing the incidence of major diseases affects GDP, labour markets and public finances. The findings will inform health policies aimed at improving population health and economic productivity.  
• Linking together biological and genetic data to understand disease progression and how this feeds through into economic consequences. The aim is to create integrated biological and economic models and use them to develop targets for resilience and effective measures of healthy life expectancy that can serve as a guide to allocating health expenditure.
• Developing state of the art economic life cycle models that integrate health and economic behaviour and allow for heterogeneity across agents. These theoretical frameworks would be used to develop evaluation methods that combine health and economic outcomes to assess which prevention methods offer most valuable returns.

Skills:

• Strong quantitative and statistical analysis skills
• Proficiency in programming languages such as Python, R, or MATLAB (Python preferred but not a necessary condition)
• Experience with econometric modelling and analysis
• Understanding of macroeconomic theory and models
• Familiarity with concepts in health economics and public health
• Data management and manipulation skills, especially with large datasets
• Critical thinking and problem–solving abilities
• Strong written and verbal communication skills for presenting complex finding
• Interdisciplinary research experience, particularly combining health and economic data
• An entrepreneurial mindset for proactively seeking out new data sources and innovative research approaches
• Data Analysis

Personalised prediction of disease risk and benefit of intervention

Project Examples:

• Building longitudinal generative AI models using multi-modal data to simulate patient trajectories under different causal interventions.
• Testing for robustness of AI models to modelling assumptions and exploring accurate measures of uncertainty quantifications.  

Skills:

• Strong background in statistical machine learning or computational statistics 
• Proficiency in modern programming language
• Experience with statistical concepts and probability (desired)
• Strong communication skills