Current global ambitions for malaria elimination focus on the margins of stable malaria transmission, where countries have enjoyed economic development, and have already made substantive progress toward international development targets. Conversely, the heartland of the global malaria burden remains entrenched in poor, low-income countries of Africa. Without a better understanding of the relationships between parasite exposure and disease outcome in these countries it will be hard to predict the impacts of current, and future interventions as part of efforts to define a global future where no one should die of malaria.

Data from a network of hospitals in Africa will be the basis of new research. Examples of how hospital data might provide insights into the changing malaria burden in Africa are already available. For example, hospital-based studies have shown a) mean ages of pediatric malaria presentation have increased with declining community levels of parasite prevalence; b) increasing proportions of severe malaria admissions with cerebral complications have been observed at some sites following an increase in community-based intervention coverage and a decrease in infection prevalence; and c) crude estimates of hospital admission rates from selected areas have been used, over short periods of surveillance, to demonstrate the inconsistencies in assuming that linear increases in vector control coverage can solely explain the changes in disease burden. However, studies already published have not used standard methodologies, preventing detailed comparison between settings.

The post will involve statistical data analysis and the development of mathematical (transmission) models with the aim of developing a comprehensive, contemporary, and standardized understanding of the relationship between parasite exposure, age and disease outcome. Models will be data-driven, including epidemiological, demographic, and clinical data. Experience with statistical / programming computer languages as well as experience with mathematical modelling are required.

The successful candidate is expected to submit publications to scientific journals, attend and present at conferences, contribute to research degree student supervision, and engage with other researchers. They will be hosted at the University of Oxford by Professor Sunetra Gupta. Work will be supervised and in close collaboration with Professor Bob Snow at KEMRI Centre for Geographic Medical Research (Kenya), and Dr Jose Lourenco at the University of Lisbon (Portugal). Occasional travel between the three institutions may be necessary.