Cardiff School of Biosciences PhD Studentship: Shining Light on Infectious Disease Dynamics – using in vivo and real-time methods to understand parasite transmission (PhD Studentship)
Reference Number: R1010
Key Studentship Information
Project Title: Shining Light on Infectious Disease Dynamics – using in vivo and real-time methods to understand parasite transmission
A NERC funded PhD studentship is available in the laboratory of Dr Sarah Perkins, Dr Jo Cable and Dr Jo Lello at Cardiff University.
The project will use the latest techniques in bioimaging to determine parasite transmission dynamics. This inter-disciplinary research will use a combination of ecology, microbiology, modelling and social network theory to track parasite transmission.
The ‘spark’ of an epidemic depends upon individual variation, with pathogen transmission more likely to occur if the epidemic is initiated by an individual that has a high number of contacts and/or is highly infectious1. We know that some individuals are highly connected (super-spreaders)2 and others are highly infectious (super-shedders)3, but we do not know who they are, or indeed if the most infected are the most infectious, and in turn if these individuals are also the most connected. During epidemics, contact patterns, exposure rates and uninfected individuals are rarely or incompletely recorded making it difficult to assess the extent to which contact frequency and infection load contribute to parasite transmission. As a solution to this, we have developed two host-parasite laboratory systems (insect-bacteria and fish-monogeneans) where infection load and contact patterns can be observed in real-time and in-vivo.
Behavioural software will be used to continuously log the duration and frequency of contacts between individuals and to construct contact networks. To determine when pathogen transmission occurs, traditional screening methods will be used in combination with bioimaging. For the latter we will use self-bioluminescent pathogens to measure non-destructively the infection load of individuals in real-time3. The student is expected to investigate fundamental aspects of epidemic dynamics using manipulation experiments to determine the following:
- What is the relevant contribution to epidemic spread of highly connected (super-spreaders) and highly infectious (super-shedder) individuals?
- Does disease control targeted at key hosts increase control efficacy?
Application Deadline: 15 February 2013.
Academic criteria: Students are expected to have 2:1 or above degree or Masters in an appropriate subject and have an interest in infectious disease dynamics and bioimaging.
Residency: To be eligible for fees and stipend of £13,590 p.a. for 2012/13(updated each year), applicants must be a UK national/ EU national who has been resident in the UK for three years prior to their application/ EU national who has migrant worker status. EU nationals who have not been resident in the UK for three years prior to application/ who do not have migrant worker status are eligible for fees only.
How to Apply
No separate application is necessary – consideration is automatic on applying for Doctor of Philosophy in the School of Biosciences (October 2013 start) through the normal university application portal.
Please indicate the project title (Shining Light on Infectious Disease Dynamics – using in vivo and real-time methods to understand parasite transmission), the supervisor (Dr Perkins) and the research centre (Molecular Biosciences) on your application in your research proposal and the funding section.
Application Deadline: 15 February 2013
For more information please contact Dr Sarah Perkins (firstname.lastname@example.org) or Dr Jo Cable (email@example.com).
- Perkins, S.E., Cattadori, I.M., Tagliapietra, V., Rizzoli, A.P., Hudson, P.J. (2003). Empirical evidence for key hosts in persistence of a tick-borne disease. International Journal for Parasitology, 9: 909-917.
- Perkins, S.E., Cagnacci, F., Stradiotto, A., Arnolidi, D., Hudson, P.J. (2009). Comparison of social networks derived from ecological data: implications for inferring infectious disease dynamics. Journal of Animal Ecology, 78: 1015-1022.