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Developing a dynamic population model of disease and accompanying open source software tool

Project Description

The University of Bath is inviting applications for the following PhD project based in the School of Management under the supervision of Professor Christos Vasilakis (, Dr Lukasz Piwek ( and Dr Richard Wood (


This research will develop a dynamic model and accompanying prototype software tool for studying how population-level incidence of disease (e.g. cancer) evolves with time. Taking into account healthcare resource capacity for diagnosis and treatment, the model will enable examining the effect of different funding strategies and clinical pathway models or treatments on patient waiting time, mortality and outcome. The model will track over time the total number of individuals in various states such as awaiting diagnostics, under treatment, end-stage, and in remission. It will also project levels of the population which are unaffected, at risk, and pre-detection. The software tool that will provide a user-friendly interface to the model, is envisaged to be open source and perhaps web-based thus aiding in achieving a wider user base. The project will be adding to the growing portfolio of externally funded, open source, R-based modelling software tools the supervisory team has been developing over the last few years (CV, RW).

Model input will include stochastic estimates relating to outflow (i.e. deaths, emigration), inflow (i.e. births, immigration), and any increasing or decreasing trends in comorbidity (e.g. obesity) and lifestyle (e.g. smoking). The model will dynamically age individuals as time progresses, accounting for the onset of various types of disease with advancing years. The simulation will work in a similar fashion to established stochastic methods (Monte Carlo), where results are aggregated from a number of runs, each performed using different generated numbers in order to capture variability and uncertainty. Model reliability will be ensured through calibrating internal parameters and dynamical relationships against published findings in the extant literature.

In mechanistically linking capacity, activity and cost with waiting time and patient outcome, the model will be useful to policy-makers for the following reasons. First, it will outline the scale of the challenge by way of projecting a “do nothing” population cohort trajectory for future years; setting out the numbers of people in each of the above-mentioned disease states in addition to waiting time and mortality rate estimates. User-controlled sliders on assumptions relating to future incidence of, say, diabetes and alcohol consumption could thereafter be used to conduct sensitivity and what-if analysis using the model. Through linking disease status to activity and spend, the cost-efficacy of interventions such as new-to-market medication, redesigned pathways, and increased capacity could be assessed. This would be of particular value to central government and care commissioners for whom balancing limited budget with performance targets is a formidable task; made more difficult through a lack of reliable information. Finally, the impact of central funding policies could be evaluated with the ability to assess the implications of any capacity constraints borne through funding restrictions.


Applicants for a studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in a relevant discipline.

Formal applications should be made via the University of Bath’s online application form:

Please ensure that you quote the supervisor’s name and project title in the ‘Your research interests’ section.

More information about applying for a PhD at Bath may be found here:

Anticipated start date: 28 September 2020.

Funding Notes

Candidates applying for this project will be considered for a University studentship, which will cover UK/EU tuition fees, a training support grant of £1,000 per annum and a tax-free maintenance allowance at the UKRI Doctoral Stipend rate (£15,009 in 2019-20) for a period of up to 4 years. Limited funding opportunities for outstanding Overseas candidates may be available. Some School of Management studentships require recipients to contribute annually up to a maximum of 133 hours of seminar-based teaching and assessment in years 2, 3 and 4 of study (students will not be expected to give lectures).

How good is research at University of Bath in Business and Management Studies?

FTE Category A staff submitted: 64.90

Research output data provided by the Research Excellence Framework (REF)

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