We know that some diseases, such as Ebola, are likely to occur in Africa but not in the UK. Some others, like seasonal flu in the northern hemisphere, occur more frequently in winter than in summer, and diseases like malaria are traditionally more common in rural, marshy lands than in cities. These are examples of how disease risk can be affected by long-term climate, weather and by the way we modify our land. We also know that other factors, like income and education have an impact on our health. These factors raise a simple question: when the environment is a key driver of diseases, is it possible to tell the risk of a disease at a certain location and at certain time, when we know the history of the local environment (for instance, average air temperature and humidity during the last month or so)? Based on our past work we are confident that for some diseases, like campylobacteriosis, this is possible. Thus, the aim of this research is to develop reliable methods to tell which and how, environmental and socio-economic factors affect the risk of infectious diseases. Once this point is properly addressed, we can then move to the next task and predict the risk of the disease under climate change scenarios. Campylobacteriosis is one of the most common bacterial foodborne pathogens worldwide. Not surprisingly, we have plentiful patients’ data from UK Health Security Agency. You will use state-of-the-art techniques from mathematical modelling and Artificial Intelligence, including natural language processing to extract relevant data from the vast literature. You will have training opportunities. You will also be expected to attend/present at UK and international conferences. You will work in a truly multidisciplinary environment (Gianni Lo Iacono, Diptesh Kanojia, Anjan Dutta, Gordon Nichols) which include collaborators from UKHSA (Raquel Duarte-Davidson). The project is opened to students with a background in physics/engineering/mathematics interested in applying their skills and knowledge for epidemiology and public health.
[1] Lo Iacono, G. et al. (2020) SSRN Electronic Journal. doi: 10.2139/ssrn.3680603
[2] Vaswani, A. et al. (2017) 'Attention Is All You ', Neural Information Processing Systems.
[3] Devlin, J. et al. (2019) 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding', doi: 10.18653/v1/N19-1423
[4] Alsentzer, E. et. al. (2019). Publicly Available Clinical BERT Embeddings. NAACL HLT 2019, 72.
[5] Hjelm, R. D. et al. (2019) 'Learning deep representations by mutual information estimation and maximization', 2019. doi: 10.48550/arXiv.2002.07017
Principle Supervisor - Gianni Lo Iacono
Dr. Gianni Lo Iacono (~40 papers in high-impact journals including PNAS and Nature Communication) is a Senior Lecturer in Biostatistics/Epidemiology at the UoS. He is a theoretical physicist by training with expertise in infectious disease modelling. Most of his research focuses on formulating and applying mechanistic and statistical models to investigate impact of environment and societal changes on diseases. In particular, he has a strong interest on the impact of weather and land use on gastro-intestinal diseases (campylobacteriosis and salmonellosis)
[Email Address Removed]
Entry requirements
Open to UK and international students with the project starting in October 2023. Note that a maximum of 30% of the studentships will be offered to international students.
You will need to meet the minimum entry requirements for our PhD programme https://www.surrey.ac.uk/postgraduate/veterinary-medicine-and-science-phd#entry.
How to apply
Applicants are strongly encouraged to contact the relevant principal supervisor(s) to discuss the project(s) before submitting their application.
Applications should be submitted via the https://www.surrey.ac.uk/postgraduate/veterinary-medicine-and-science-phd#apply programme page (N.B. Please select the October 2023 start date when applying).
You may opt to apply for a single project or for 2 of these Faculty-funded studentship projects
When completing your application, in place of a research proposal, please provide a brief motivational document (1 page maximum) which specifies:
- the reference numbers(s) for the project or two projects you are applying for
- the project title(s) and principal supervisor name(s)
- if applying for two projects, please also indicate your order of preference for the projects
- an explanation of your motivations for wanting to study for a PhD
- an explanation of your reasons for selecting the project(s) you have chosen
Additionally, to complete a full application, you MUST also email a copy of your CV and 1-page motivational document directly to the relevant project principal supervisor of each project you apply for. Due to short turnaround times for applicant shortlisting, failure to do this may mean that your application is not considered.
Please note that online interviews for shortlisted applicants are expected to take place during the week commencing 30th January.
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