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.
Aim: 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 the following over-arching 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, we 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 (e.g. Leptospirosis).
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 MetOffice (see http://tools.data-mashup.org.uk/medmi/
). 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/or VITO satellite images for land use data). Appending animal health data to these resources is an important step that should enable further exploitation for animal disease.
Methods. Data alone cannot explain/predict the different stages shown in the figure. 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 (why) 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:
i) Generate specific hypotheses about the underlying mechanisms,
ii) Translate the mechanism into a process based model
iii) Test if the predictions reproduce empirical patterns.
Finally, we 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.
The student will work under the supervision of Dr Gianni Lo Iacono and Prof Alasdair Cook and in collaboration with Prof Gordon Nichols and other members OHEJP consortium.
This is an interdisciplinary project requiring computational and mathematical skills as well as a good understanding of 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 is desirable but not essential.
If English is not your first language, you will be required to have an IELTS Academic of 6.5 or above (or equivalent), with no sub-test score below 6.
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). Please clearly state the studentship title on your application. Applicants are invited to contact Dr Lo Iacono to discuss the project informally prior to making an application.