Novel methods in data science to quantify viral and environmental triggers of chronic disease exacerbations

People living with chronic diseases are at risk of early death, and suffer significant illness due to these diseases. Worsening periods of health, or "exacerbations" of asthma, chronic obstructive pulmonary disease, and coronary heart disease are costly and are bad for patients. Infection by a virus (like a cold, or 'flu) is known to trigger exacerbations, but there are other factors, like air pollution, and low or high temperatures that can also trigger them.

Despite knowing the importance of viruses, there is not good understanding of how circulation of viruses in the general population affects the timing of exacerbations at the level of a city or town. This also makes it hard to estimate the effect of other triggers, like air pollution, which is urgently needed.

This study will analyse the patterns of exacerbations, including respiratory virus circulation in the general population as well as non-infectious triggers, such as air pollution.

This project focuses on exacerbations (periods of worsening) of three major diseases in the UK: asthma, chronic obstructive pulmonary disease, and coronary heart disease. People with these chronic diseases are at risk of early death, and suffer significant morbidity as a result of their conditions. Viral infections are known to trigger exacerbations from individual-level studies, but there is limited understanding of how circulation of triggering viruses impacts population-level patterns of exacerbations. A key tool for interrogating infectious disease dynamics - infectious disease modelling - has not been used to understand the role of infections in population-level patterns of chronic disease exacerbations. Current methods either inaccurately include infectious variables, or use statistical smoothing to remove their effect, leading to unreliable estimation of other triggers. Reliable estimation of the effect of risk factors is crucial for policymakers, and for patients. The accepted best tool for understanding infectious disease dynamics is transmission modelling. I aim to close this gap in our knowledge by using state of the art transmission modelling techniques to better quantify the burden of infectious disease on chronic conditions. By bringing together two disciplines and using novel methods, this project will strengthen the evidence base for possible interventions, such as vaccinations, preventive care, risk awareness, and treatment options.