The Centre for Data & Statistical Science for Health aims to facilitate research and knowledge exchange in data and statistical science, with a focus on translations into innovative solutions to major global health problems. Our work is divided around three broad areas: methods, data, and applications. Within each of these areas, we have topics of focus which highlight our major projects or areas of research.
Methods
Members of the Centre are working at the cutting-edge of new methodologies of data and statistical science, developing and contributing to new approaches within the field.
In the tabs below, you can find a list of the key data and statistical science methods that our members use in their research.
- Statistical methodology
At the Centre, our members lead on developing, and finding the best, statistical methods available to analyse health data. Examples of statistical methodology used by our members includes:
- Causal inference methodology
- Missing data
- Trials methodology
- Epidemiological methods
Our Centre includes epidemiologists who study how, and why, diseases and health problems are distributed in a given population. Some examples of epidemiological methods our members use include:
- Target trial emulation
- Triangulation
- Study designs
- AI & Machine Learning for health
As AI and machine learning techniques have developed in recent years, our members have begun exploring and applying these tools to their research, where appropriate. Our members work closely with the following AI methodological tools:
- High-dimensional propensity scores
- Natural language processing agents
- Molecular LLMs
- Federated learning
- Multi-omics and bioinformatics
Bioinformatics uses computer science techniques to analyse complex biological data, most notably multi-omics data, which encompasses the "omes" of biology: genomes, epigenomes, proteomes, trascriptomes and more.
Our members use multi-omics and bioinformatics in these areas:
- Phylodynamics
- Genomic epidemiology
- Microbiome
- Proteomics
Data
Our Centre's members use, and analyse, various types of data to advance their research and tackle public health problems. Each approach provides different perspectives in tackling public health problems.
The tabs below explores the data our memebrs use in their research.
- Electronic Health Records data
As health care and administrative systems systematically collect their routine health data in a digital format, our members can analyse these data to tackle questions around human health and disease. Some examples of projects and groups using electronic health data include:
- Data linkage & infrastructure
Our members support efforts to develop, and maintain, the essential infrastructure required to share and analyse health data. Some examples of partners that our members work with for data linkage and infrastructure:
- Cohorts & health databases
Examples of projects and areas of research include:
- Biobank
- Cancer audits
- International collaborations
- Data collections from the MRC Units
- Specialist data for health
Alongside the above, some of our members work with specialists types of data to tackle challenges within specific fields of public health. This includes:
- Pathogen genomics
- Spatial
- Nutrition
Applications
In line with our Centre's mission for translation of our expertise into innovative solutions, our members apply their expertise to a range of problems within global public health.
Some examples of the most prominent applications that our members focus on are in the below tabs.
- Climate change & environmental health
Studying how environmental factors, and different environmental exposure to health, is becoming more important than ever as we navigate climate change.
As part of this research, some of our members also work closely with LSHTM's Centre on Climate Change & Planetary Health. Some examples of projects and collaborations include:
- Clinical & pharmaco-epidemiology
One topic that some of our members apply their expertise is within problems that cover one of two fields in epidemiology: clinical problems or pharmaco-epidemiology research looking at the benefits and risks of medical drugs at a population level.
- Global public health
Many of our members work to tackle public health issues in, and across, different countries' throughout the world. Some topics of focus include:
- Social science
- Infectious diseases
Data and statistical science methods are applied to infectious disease research in various different ways, including:
- Surveillance
- Epidemiology
- Mechanisms of pathogenesis
- Drug discovery
- AMR