Bayesian evidence synthesis, infection disease modelling, Electronic Health Records, and Public Health.

Chronic obstructive pulmonary disease (COPD) is a respiratory condition that has high morbidity and mortality, and a growing global burden. Exacerbations are acute worsenings of the symptoms of COPD, which often result in hospitalization, progression of symptoms, and sometimes death. Exacerbations can be caused by respiratory viruses, poor air quality, temperature, and other factors. This PhD project will use electronic health record data from a study site in Nha Trang Vietnam, together with respiratory virus surveillance data from the same area, to understand the links between COPD hospitalization, viral circulation, and other factors.

The project will develop mathematical transmission models for key respiratory virus(es) and fit those to data. The project will develop a Bayesian estimation framework to quantify the role of infectious and non-infectious factors in observed COPD exacerbation time series. The project aims to understand the interplay between these factors and may be able to assess or design control strategies to decrease the burden of COPD in this area.

LSHTM supervisors: Rosalind Eggo, Liam Smeeth
Nagasaki supervisors: Lay Myint Yoshida, Michiko Toizumi