The interaction between host demography and tuberculosis transmission: disease dynamics, control and evolution.

TB is the leading single cause of death in humans due to an infectious agent. Approximately 30% of the world's population is latently infected and 0.1% has active disease. TB is characterised by long-term asymptomatic infection that can progress to active disease years or decades after exposure. Active disease can produce a persistent cough, fever, weight loss and, if untreated, death in approximately 50% of cases. In recent years rates have escalated due to the interaction with the HIV epidemic and the emergence of drug resistant strains. TB affects many other mammals as well as humans. Bovine TB is endemic in many domestic cattle populations. Transmission to humans can occur via contaminated milk or direct contact. In Great Britain, bovine TB is one of the most complex and persistent problems facing the cattle industry, with an estimated annual cost of 90 million. Transmission of bovine TB between farms occurs via a combination of local infection and the movement of infected animals. While much less is understood about the natural history of disease in cattle, many of the principles involved in understanding TB spread apply to both humans and cattle. Mathematical models are used to predict how TB will spread in a population and the kind of interventions that might lead to eradication. Models include factors that are known to affect the spread of TB such as stress or overcrowding. Furthermore, progression rates are highly dependent on age of infection. This means that an ageing population is very different, in terms of TB, than a population with high birth rates. The effect of changing demography on TB spread has not been investigated in TB models, and so predictions about how to control the disease are not as accurate as they could be. This project will develop a new, comprehensive framework to understand the transmission dynamics and evolution of chronic diseases, with a particular focus on TB. The framework will be flexible to the needs of TB epidemiologists by incorporating transmission, age-dependent immune response, demographic changes and variation in genetic susceptibility.

During the course of the fellowship, I propose to undertake a body of work that will advance current thinking about modelling and control of chronic diseases. The initial impact of the work will be aimed at the academic community through publishing articles in the modelling and interdisciplinary literature, attending conferences and establishing collaborations with researchers. This research is fundamentally interdisciplinary, and therefore will impact both theoretical and applied research. In particular, the new knowledge will be shared with infectious disease modellers, demographers, TB clinicians and epidemiologists, veterinarians and researchers studying bovine TB transmission in cattle and wildlife populations. I will maintain a web server with background information and major findings. This will include the framework and code for basic TB models, so that modelling can be included in field studies at an early stage by non-expert modellers. The advances in understanding TB population dynamics will have direct implications for public health and the control of livestock diseases and will contribute to maintaining the UK's position at the forefront of infectious disease modelling and disease control. Collaboration with the World Health Organization (WHO), the Health Protection Agency (HPA) and the Department for Environment, Food and Rural Affairs (Defra) will be an essential means of realising the maximum long-term health and economic benefits of this work. Promotion of my work in public spaces, such as at museums and online, will increase its access for a wide range of people. Presentations and posters at agricultural and countryside shows and events will improve the awareness of the research among farming communities and a presence at medical conferences and publication in medical journals will facilitate communication with the healthcare industry.