Forecasting personal health in an uncertain environment

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 bold and ambitious proposal is to bring about a step change in both 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. This project is unique in that it combines mechanistic models of physiology with the modelling personalised medicine with the integration of uncertainty at all scales.

We recognise that our vision is bold and ambitious. 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 prevalent 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. A conventional approach to model transmission of influenza would take into account contact between individuals, with a probability of infection passing from one to another. Our approach will be to unpick this probability, replacing it with a model of the likelihood of infection based on knowledge of the health status of an individual (do they smoke, do they have asthma, etc.), coupled with population variability (for example of airway geometry, a key determinant of how inhaled particles are transported to the mucus layer of lung airways). A conventional approach to modelling atrial fibrillation in the heart would take into account the geometry of the human atrium, combined with a trigger. Remodelling of the structure and function of the atria would be imposed on the model. Our approach will be to parameterise a model of atrial fibrillation using data from longitudinal studies, and use output from the model to determine the treatment that would have been most beneficial for that patient.

Among the many challenges faced by the healthcare system, clinical decisions informed by incomplete information can lead to low treatment success rates, inefficient use of resources and poor patient outcomes. This proposal seeks to enable clinicians to make better use of patient-specific data, informed by mechanistic modelling. We seek to push multiscale physiological modelling to a new level of sophistication, by incorporating uncertainty and variability systematically across different scales of organization and translating the outcomes to the clinic. Our ambition is for our approach to find utility across the healthcare sector: the clinician will be able to make better-informed forecast of outcomes for each patient; policy decisions will be informed by population models; pharma companies will be able to assess the efficacy of drugs on individuals; and the patient will enjoy improved outcomes.

The beneficiaries of this research include the mathematics, healthcare modelling, and physiological modelling academic communities, as well as clinicians and healthcare professionals, manufacturers of healthcare devices and technology, and ultimately patients and the general public.

The main non-academic professional beneficiaries are clinicians and medical practitioners, as well as companies and businesses developing and manufacturing healthcare technology. In the long-term this may also include the pharmaceutical industry. These groups will benefit from this research because this project will contribute to the translation of modelling technology from a research tool to a widely accepted tools for use in clinical practice. Clinicians and other healthcare professionals will be able to deliver improved diagnosis interventions, and manufacturers of healthcare equipment could benefit from the competitive advantage provided by diagnostic or interventional systems that embed uncertainties.

As these ideas are deployed within healthcare, then patients and the general public will benefit from this research.