Environment and disease: A general method linking mechanism and phenomenology
Funding will cover University fees at the UK/EU rate for three years and a stipend for three years at RCUK levels (£15,000 per year). In addition, funding includes bench fees to a value of £10,000 over the three years to cover conference attendance.
Funding sourceOne Health European Joint Programme and Zoetis
The impact of environment on infectious diseases is well known. It can affect pathogen abundance, survival, and virulence, host susceptibility to infection as well as human behaviour.
The aim of this project is to develop a general tool to assess the risk of infectious diseases (in particular zoonosis) when we have information of relevant environmental factors.
Accordingly, we are interested in answering the following questions:
- Can we identify and access 'big data' – existing information that can be interrogated to yield new evidence for decision-making in One Health? The generation of new analytical approaches would provide tools that could then be adapted to specific animal or human health issues where environment plays a key role in aetiology.
- Can we identify the key environmental processes triggering and propagating zoonoses?
- Can we disentangle the role of animal, human (including socio-economic factors) and environmental factors in zoonoses?
- Can we identify the delay between variations in the environment (e.g. increase in the temperature or behavioural change) and the occurrence of a foodborne outbreak?
- How can we quantify their impact on Animal and Public Health?
As proof of concept, you will use salmonella, for which we have plenty of data from Public Health England (PHE). This case study will be used to validate our approach going forward.
A huge advantage is the high spatio-temporal resolution of data available through the MEDMI platform, a resource linking the data from PHE with data from the Met Office. Land use and socio-economic data are being collected as part of on-going projects (e.g. from APHA for livestock data, Centre for Ecology and Hydrology and VITO satellite images for land use data). Appending animal health data to these resources is an important step that should enable further exploration of animal disease.
Data alone cannot explain or predict the different stages however. We have developed new methods to estimate the probability that a particular disease occurs, knowing recent environmental parameters at a certain location (e.g. average temperature and humidity during the last few weeks). Our preliminary analysis shows that knowledge of this probability allows accurate prediction of the risk of diseases and their temporal patterns. A critical limitation of this statistical approach is that it can only tell how the environment impacts on foodborne diseases, but here we want to understand the causes of the particular dependency of these probabilities on the environmental factors. To address this point we will merge the statistical approach already developed, with mechanistic, population models for salmonella. Accordingly, we will:
- Generate specific hypotheses about the underlying mechanisms
- Translate the mechanism into a process based model
- Test if the predictions reproduce empirical patterns.
Finally, you will use this integrated mechanistic/statistical approaches to address the over-arching questions above. The same approach, once validated, will be adapted and applied to leptospirosis, taking into account data from animal surveillance as well as the other data above mentioned.
Related linksOne Health European Joint Programme Zoetis MEDMI Climate-Health Tool
This is an interdisciplinary project requiring computational and mathematical skills as well as a good understanding of biological processes.
You are required to hold an undergraduate degree in mathematics or a related subject (e.g. physics or engineering). Undergraduates with a degree in biological sciences or a related subject are also welcomed as long as you have a strong interest in mathematical modelling. A masters degree in a public health or epidemiological-related subject is also desirable. Experience in mathematical modelling and biostatistics is desirable but not essential.
How to apply
To be considered for this studentship you will first need to apply for our Veterinary Medicine and Science PhD. Within this application, please clearly mention this studentship to be considered.
Veterinary Medicine and Science PhD