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Using Complex networks and Machine Learning to Model Epidemic Spreading in Cattle Herds, Computer Science – PhD (Funded)

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  • Full or part time
    Prof R Menezes
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences, is inviting applications for a fully-funded PhD studentship to commence in September 2019 or as soon as possible thereafter. The studentship will cover UK/EU/International tuition fees plus an annual tax-free stipend of at least £15,009 for 3.5 years full-time, or pro rata for part-time study. The student would be based in Computer Science in the College of Engineering, Mathematics and Physical Sciences at the Streatham Campus in Exeter.

As the world consumes more meat and derived products, markets and producers are put in a position of increased pressure to deliver their products efficiently. In a global market, meat products produced in one part of the world can easily find its way across the globe. In order to fulfil demand, livestock producers are constantly moving the animals from place to place so that they can graze in new pastures, or because of trade, or even to have the animals taken to slaughterhouses. As we increase the movement of cattle within countries and even across countries, we also increase the chance of global pandemics which could cause huge financial losses to producers while making consumers pay a higher cost due to scarcity of the product. Epidemic spreading is an active area of research, and computer and analytical models of mobility have been successfully used in the prediction of human diseases such as H1N1, Ebola, Flu, etc. Such modelling may be also used to capture the idiosyncrasies of cattle mobility and help us prevent financial losses due to the spread of foot and mouth (FMD), brucellosis and others. This project aims at modelling the mobility of cattle in Brazil with the intent of helping prevent disease spread in that country. Due to the scale of the datasets and the nature of the country (continental size), livestock transportation in Brazil may include illegal transportation due to the difficulty in auditing all movement. Illegal trade of livestock may hinder any effort of curtailing disease spread; if the amount of illegal trade is large enough, official measures of containment are nullified. The project will look at modelling illegal cattle trade and the effect of this to epidemic thresholds. Moreover, given the continental size of the country, the modelling also has to carefully consider variables such as: mode of transportation, quality of roads, cross-contamination due to the sharing of vehicles, etc. The outcome of this of this project is a more general model of epidemic spread in cattle which can serve as the basis for other livestock modelling. The work is quite multidisciplinary and will involve partners in Brazil. This work also links well with the current global effort to understand cattle movement at the world-wide level.

The applicant should be willing to work with data science models, machine learning, network science, python programming language (desirable), statistical modelling and have a strong enough background in computer science and maths to enable the applicant to carry independent research.


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