Cystic echinococcosis (CE) is a zoonotic parasitic disease of significant public health concern in many parts of the world. For example, over 5,000 new CE cases are reported in South America every year and a recent study has estimated that more than 150,000 people in Bulgaria, Romania and Turkey might be affected by CE.
The burden, extending to include economic impacts, is mostly felt in subsistence livestock keepers and other marginalised rural and peri-urban populations where other health competing threats persist.
Risk classification and ranking of administrative units are frequently used to help prioritization of resources for disease control. Whereas risk is a key parameter to inform resource allocation, it often fails to explicitly reflect the level of existing preparedness against the threat of concern, CE in our case. The large number of activities contributing to disease control capability, and their likely heterogeneous implementation across the units of interest, make comparisons of capability performance, for example among administrative units, a complex task.
Our key research objectives are:
1. The derivation of a comprehensive composite metric of CE susceptibility, and through this, identification of areas of vulnerability and risk where additional investment in control is warranted.
2. Solve the complex allocation of scarce resources as captured by the large matrix of multiple intervention and surveillance options across multiple spatial units with very different susceptibility baselines (susceptibility estimated from the previous obejctive).
The model will be developed using data from Rio Negro, Argentina initially, which will then be extended to the whole country and data from various East European countries (such as Bulgaria). The student will work under the supervision of Dr. Joaquin Prada and Dr. Victor del Rio Vilas at Surrey, in collaboration with Dr. Adriano Casulli from ISS (https://www.iss.it/
) and Prof. Edmundo Larrieu.
This is an interdisciplinary project requiring computational and mathematical skills as well as an interest in biological processes.
Applicants are required to hold an undergraduate degree in Mathematics or a related subjects (e.g. Physics, Engineering). Undergraduates with a degree in Biological Sciences or a related subjects are also welcomed as long as they have a strong interest in mathematical modelling. A Masters degree in a public health or epidemiological-related subject is desirable. Experience in mathematical modelling, biostatistics or machine learning is desirable but not essential.
How to apply:
Please apply for this PhD through the School of Veterinary Medicine PhD applications portal https://www.surrey.ac.uk/postgraduate/veterinary-medicine-and-science-phd
(click on the “Apply” tab). Applicants are invited to contact Dr. Prada to discuss the project informally prior to making an application.