Computational Modelling Group

Healthcare modelling

Operational Research (OR) has existed as a scientific discipline for over 70 years and has been applied to healthcare for over 50 years. The UK OR Society and the National Health Service (NHS) held a joint Colloquium on hospital appointment systems back in 1962. Since then OR models have been widely and successfully used to assist clinical decision-making, facility planning, resource allocation, evaluation of treatments, and organisational redesign. One of the most commonly used approaches is computer simulation, widely regarded as the technique of choice in healthcare because of its power and flexibility as well as its ability to function at a range of scales from the individual patient up to whole-system models.

Since its inception OR has been a discipline focused on solving real-world problems. Addressing the problems of real hospitals and real patients is paramount. Modellers need to develop new approaches to tackle the tough cultural problems inherent in healthcare systems. Multi-disciplinary working is "de rigeur" - Operational Researchers need to work alongside clinicians and healthcare managers as well as other disciplines such as health services research and health economics in order to exploit the synergies between them.

Healthcare modelling is an endlessly fascinating and challenging area in which to work. It is hard to imagine a more complex organisation than the NHS (the largest employer in Europe and the third largest worldwide, allegedly only exceeded by the Indian Railways and the Chinese Army!). OR models have been used at a patient or population level for modelling epidemics and disease progression, as well as at the micro-level for physiological processes and pharmacodynamics, for example. There are countless research challenges: developing models acceptable to all users, balancing user-friendliness with scientific rigour and validity, capturing human behaviour, understanding the complex links between humans and systems, not to mention all the clinical aspects. New PhD students are always welcome, from any numerate background!

For queries about this topic, contact Sally Brailsford.

View the calendar of events relating to this topic.


Bayesian Agents as Models for the Disclosure Behaviour of Pregnant Drinkers

Seth Bullock, Jakub Bijak (Investigators), Jonathan Gray

Examining the feasibility of signalling games, played by Bayesian decision theoretic agents as a model for the disclosure of drinking behaviour by pregnant women to their midwives.

Care Life Cycle

Seth Bullock, Sally Brailsford, Jason Noble, Jakub Bijak (Investigators), Elisabeth zu-Erbach-Schoenberg, Jason Hilton, Jonathan Gray

This research programme brings together teams of researchers from social sciences, management science and complexity science to develop a suite of models representing the socio-economic and demographic processes and organisations implicated in the UK’s health and social care provision. Integral to the project is working with our partners in the public sector and communicating the results of these models to policymakers allowing them to effectively plan for the future.

Mathematical tools for analysis of genome function, linkage disequilibrium structure and disease gene prediction

Mahesan Niranjan, Andrew Collins, Reuben Pengelly (Investigators)

This iPhD project uses a Gaussian Bayesian Networks framework through Machine learning methods to predict which genes are involved in the development of different diseases.

Mathematical tools for analysis of genome function, linkage disequilibrium structure and disease gene prediction

Andrew Collins, Mahesan Niranjan, Reuben Pengelly (Investigators), Alejandra Vergara Lope

This iPhD project uses a Gaussian Bayesian Networks approaches framework through machine learning approach to predict which genes are involved in the development of different diseases.

Respiratory mask modeling

Jacques Ernes

Abaqus modelling of repiratory masks, bioengineering, Health sciences

Simulating Sleeping Sickness: a two-host agent-based model

Jason Noble, Peter Atkinson (Investigators), Simon Alderton

Sleeping sickness is a vector-borne, parastic disease which affects millions of people across 36 sub-Saharan African countries. Using agent-based models, we aim to gain a greater understanding of the interactions between the tsetse fly vector and both animal and human hosts.

Building an accurate representation will allow the testing of local interventation scenarios including the closing of watering holes, and the selective spraying of cattle with insecticides.


Peter Atkinson
Professor, Geography (FSHS)
Jakub Bijak
Professor, Social Sciences (FSHS)
Sally Brailsford
Professor, Management (FBL)
Seth Bullock
Professor, Electronics and Computer Science (FPAS)
Mahesan Niranjan
Professor, Electronics and Computer Science (FPAS)
Paul Skipp
Reader, Biological Sciences (FNES)
Stefanie Biedermann
Lecturer, Southampton Statistical Sciences Research Institute (FSHS)
Reuben Pengelly
Lecturer, Medicine (FM)
Jason Noble
Research Fellow, Electronics and Computer Science (FPAS)
Simon Alderton
Postgraduate Research Student, Geography (FSHS)
Jonathan Gray
Postgraduate Research Student, Social Sciences (FSHS)
Tom Hebbron
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Jason Hilton
Postgraduate Research Student, Social Sciences (FSHS)
Alejandra Vergara Lope
Postgraduate Research Student, Engineering Sciences (FEE)
Davide Zilli
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Elisabeth zu-Erbach-Schoenberg
Postgraduate Research Student, Management (FBL)
Elena Vataga
Technical Staff, iSolutions
Petrina Butler
Administrative Staff, Research and Innovation Services
Ella Marley-Zagar
Enterprise staff, Medicine (FM)
Mohsen Mesgarpour
Alumnus, University of Southampton
Jacques Ernes
External Member, Technical University of Eindhoven