Our aim is to facilitate interdisciplinary research and develop new approaches in data and statistical science to find innovative solutions to major global health problems.
Themes
The Centre has six interlinked themes:
Data research
Focusing on methodologies related to data collection, visualisation, linkage, curation and dissemination.
Statistical methodology
Developing and evaluating state-of-the art statistical and machine learning techniques to address important questions in global health.
Routine data
Concentrating on the research opportunities offered by routinely collected health data and on developing improved ways of accessing and using such data.
Molecular and imaging data
Encompassing large-scale 鈥榦mics data, pathogen and diagnostic imaging, immunological and serological data and the full spectrum of pathogen-related data and metadata.
Social science data
Highlighting the contribution of qualitative and quantitative social science data to global health research, with an emphasis on anthropological data.
Challenges
The Centre considers four major interrelated challenges:
Understanding the major determinants of health
Exploring the use of large-scale linked data to gain insights into the wider social and environmental determinants of health.
Developing and evaluating diagnostics and interventions
Focusing on the role of molecular, imaging and related patient health data in the development of new and improved diagnostics and interventions.
Informing health policies that improve population health and reduce inequities
Applying developments in statistics and machine learning to provide timely evidence to inform changes in policy and practice.
Improving environmental and planetary health
Addressing the need for the development of novel analytical techniques and data science methods in the field of climate change and planetary health.
