Assessing the impact of climate and land use change on the risk of mosquito vector-borne disease emergence in Scotland.
Supervisor: Professor Heather Ferguson, School of Biodiversity, One Health & Veterinary Medicine
Rotation project:
Mosquitoes are the most important source of vector-borne diseases (VBDs) globally on account of their role in transmitting high burden human diseases, zoonoses and veterinary pathogens. While the burden of mosquito VBDs is generally highest in tropical and subtropical regions, there has been a considerable expansion of some mosquito VBDs into temperate zones, including the UK, due to international trade, travel and environmental change. In addition to native vectors such as Culex pipiens (vector of West Nile virus), the invasive Aedes albopictus (vector of Dengue, Chikungunya and Zika viruses) has been recently detected in England, as well as bird populations infected with mosquito-borne zoonoses. However, surveillance of these vectors and pathogens in the UK has been mostly limited to England and Wales, with little data for Scotland. As part of a large UKRI-DEFRA programme, we are establishing the first nationwide surveillance for mosquitoes and vector-borne pathogens in avian reservoir populations in Scotland. This will project will make use of data collected from this programme, in combination with previous surveillance and citizen science data to predict the distribution and abundance of mosquito vectors species in Scotland under current and future climate scenarios. These models will use cutting edge hierarchical Bayesian joint species distribution models to predict potential hotspots of VBD emergence in Scotland, and guide the establishment of longer-term surveillance. We will work directly with partners in the UK and Scottish Government to model specific scenarios of interest for policy making on climate change and health. While this project will concentrate on quantitative analyses and modelling, there will be opportunities to participate in field and lab work. The ideal candidate for this PhD position should possess a strong background and interest in quantitative analyses, geostatistical modeling, geographic information systems (GIS), disease ecology, One Health, epidemiology, and epidemic preparedness. Throughout the course of the research, the candidate will have the opportunity to acquire and refine advanced statistical modelling techniques, and data integration and analysis by working with diverse datasets. Collaboration with government partners will enhance the candidate's ability to engage with stakeholders and translate research into evidence-driven recommendations for policy-making.