Acute febrile illness causes substantial burden in Caribbean, but the causes and predictability of outbreaks are not well understood. This project will combine mathematical and statistical models with a range of novel data sources – including surveillance data, community serological surveys, large-scale human mobility data and environmental factors – to investigate the role of influenza and dengue in driving acute febrile illness outbreaks in Dominican Republic, and develop better methods for forecasting such diseases.

The project will provide an opportunity to develop and expand skills in: modelling of infectious diseases, Bayesian inference, as well as quantitative analysis of large data sets, evidence synthesis, and forecasting techniques.

The project will require some prior knowledge of epidemiology and statistics. Experience of programming, for example with R, and of using mathematical and/or statistical models would also be beneficial.