Enhancing neglected disease control through geostatistics

Neglected tropical diseases (NTDs) are a diverse group of diseases that cause considerable human, social and economic burden largely borne by impoverished communities. They are a target for global action in the Sustainable Developments Goals, but many low and middle income countries remain endemic for NTDs and need to strengthen their control strategies to meet the World Health Organization (WHO) targets for control and elimination by 2030. A factor hindering progress towards the control and elimination of NTDs is the conventional methods used to evaluate disease prevalence and inform control strategies, as they do not adequately account for spatial aspects of disease epidemiology and the environmental and climatic drivers of disease transmission.

Geostatistics incorporates a collection of powerful statistical methods that can be used to evaluate spatial correlations of disease related to climatic, environmental and animal or human population factors. This is particularly advantageous in the context of NTDs that are often reliant on the ecological niches of the infectious agents and vectors that drive disease transmission.

This project will capitalize on existing and developing NTD studies across Africa and Asia-Pacific to develop detailed predictions of NTD prevalence and burden that will better inform control strategies. Furthermore, geostatistical adaptive designs will be used to design subsequent NTD surveys to evaluate the impact of control strategies and inform subsequent control programs as countries strive towards the WHO 2030 targets for NTD control and elimination.

This project is open to Domestic and International applicants and would suit someone with a passion for infections diseases and global health. Experience in epidemiology or biostatistics and in analysing large-scale datasets is preferable.

How to Apply

Express your interest by emailing Dr Adam Bartlett at abartlett@kirby.unsw.edu.au. Include a copy of your CV and your academic transcript(s).