Forecasting personal health in an uncertain environment

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Healthcare delivery takes place in a noisy and uncertain environment. Some patients present following their first symptom, whereas others may wait before presenting to a healthcare provider. Some patients comply with and respond to treatment, while others don't.

Forecasting the health status of an individual is consequently difficult without the crude use of random numbers to determine risk. On the other hand, mechanistic models of human physiology show promise as research tools, but are difficult to apply effectively to healthcare problems because there is no framework to embed the variability in structure and function that is seen in individuals and human population.

The challenge of this ambitious project is to bring about a step change in modelling for healthcare by developing new mathematical tools that embed uncertainty at every level, enabling models that describe the trajectory of an individual through healthcare to be informed by detailed mechanistic and multiscale models of organ systems.

As a "way in" to this problem we have selected two specific exemplars where the project partners already have expertise and access to models, tools, and data. The first of these is the transmission of influenza within a population, and the second is a common cardiac arrhythmia (atrial fibrillation). We will adopt a "middle out" approach, and will start at the patient (individual) scale where there are rich data for each exemplar. From this point we will work upwards to population scale, and downwards to the molecular scale.

Click on the resources link (above) for papers and software arising from the project.

 EPSRC logo and link to www.epsrc.ac.uk

QUINTET is EPSRC grant EP/K037145/1
University of Sheffield logo and link to www.sheffield.ac.uk Insigneo logo and link to www.insigneo.org
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