Dynamic zoonotic disease modelling for environmental change
This NERC PhD will develop novel computational methods to integrate zoonotic disease transmission from animals to humans over changing landscapes and climates to predict impacts of global change on disease burdens. This NERC PhD is part of QMEE (Centre for Doctoral Training in Quantitative and Modelling Skills in Ecology and Evolution).
About this PhD Project
Human infectious diseases are a significant threat to global human health and economies (e.g., Ebola, SARS), with the majority of infectious diseases having an animal source (zoonotic) (Jones et al. 2008). Despite their public health relevance, many important diseases have not been systematically studied from a quantitative perspective, limiting our understanding of how spillovers of zoonotic infectious diseases into the human population are impacted by global and local environmental stressors (Whitmee et al. 2015). Furthermore, for most diseases little is known about how climate change, anthropogenic landscape alteration and changing populations will impact on future infectious disease outbreaks (Hotez & Kamath 2009). There is therefore an urgent need for a more interdisciplinary approach integrating computational modelling, ecology and health towards a holistic understanding of disease dynamics.
In this interdisciplinary PhD project, we bring together leading computational, ecological and epidemiological expertise to develop a new integrated framework of disease modelling with a case study of Lassa fever (LF) in West Africa, a devastating viral haemorrhagic disease which has severe impacts on some of the world's poorest communities. This PhD project will combine ecological models of animal reservoir, spillover and human disease spread (Redding et al. 2016) in a single probabilistic modelling framework which will enable full uncertainty quantification and prediction. Importantly, our approach will leverage recent developments in the statistical computing community (Schnoerr et al. 2016) to retain a higher level of mechanistic detail that is currently possible within epidemiological models. This will enable us to provide model-based predictions of responses to a changing environment, and to evaluate the impact of intervention strategies in plausible future scenarios. This project will work in close collaboration with the Institute of Global Health at UCL and partners at The Centre for Disease control for Nigeria and the west African hub of the African Union to embed the research into policy and priority setting within the stakeholder communities across west Africa.
Training & Skills
This project will use a diversity of quantitative skills. During data collation the student will gain experience using GIS and spatial statistics when preparing spatial input layers and machine learning when building models to describe host presence. Then the student will then be trained in implementing probabilistic agent-based modelling techniques to simulate contact rates and pathogen spill over, alongside stochastic, discrete-time simulations of subsequent infections and disease progression. The project also offers extensive transferrable skills including stakeholder engagement and knowledge exchange. Predicting how zoonotic diseases will be impacted by future global change addresses a significant public health issue. It is thus anticipated that the results of this project offer substantial scope for practical impact and will attract much attention.
Logistics & Application
The project will be primarily based at Zoological Society of London/University College London. It will be supervised by Prof. Kate Jones Zoological Society of London/University College London, and Prof. Mark Girolami and Prof. Christl Donnelly at Imperial College, Dr Guido Sanguinetti, University of Edinburgh. Studentships will last for 3.5 years full-time or the equivalent period part-time. Most applicants will have, or be about to obtain, a Masters qualification (MSc, MRes or MSci/MMath) and a 2.1 or higher undergraduate degree. Exceptional students at Bachelors level without a Masters will also be considered. Relevant post-graduate experience will also be taken into account.
For more information or to apply please send your CV and a covering letter (including references) stating your suitability for the project to Prof Kate Jones (email@example.com) by 5pm on 19 January 2017.
This PhD is part of QMEE (Centre for Doctoral Training in Quantitative and Modelling Skills in Ecology and Evolution), a Doctoral Training Programme. At least 12 fully-funded PhD studentships will be offered by QMEE. This is a 3.5 year PhD studentship (for full-time students or pro rata for part-time students) with a stipend set at the RCUK national rate (forecast to be £14,296) with an anticipated start date of October 2017. To be eligible for a full award a student must be a UK citizen or have been resident in the UK for a period of 3 years or more. If you are a citizen of an EU member state you will eligible for a fees-only award, and must be able to show at interview that you can support yourself for the duration of the studentship at the RCUK level.
• Jones et al. 2008 Nature 451:990
• Whitmee et al. 2015 Lancet 386:1973
• Hotez & Kamath 2009 PLoS Negl Trop Dis 3:e412
• Redding et al. 2016 Methods in Ecol. & Evol 7:646
• Schnoerr et al. 2016 Nature Comms 7