This project will use computational methods to assess risks.
An expectation of the society we live in is that the food that we eat is safe. That is
- it does not contain harmful bacteria (e.g. Listeria, Salmonella, Campylobacter, E. coli O157) or their toxins (e.g. aflatoxins, botulinum and staphylococcal toxins) that can cause disease.
- it does not include allergens (e.g. peanuts, sesame seeds etc) unless they are declared on the packaging.
- it does not contain levels of chemical contaminants (e.g. pesticides. PCBs, dioxins, plastics etc,) that are detrimental to health either now or in the future.
But how do you determine whether food is safe? A possible way is to check foodstuffs by microbiological or chemical methods for the contaminants mentioned above. However, it is not practical to test every batch of food for every possible contaminant. Hence, there is a need to assess risk without having complete information about possible contaminants in every piece of food. But what is risk?
Risk comprises two different aspects. The first is the probability that the contaminant is in the food. The second is the severity – how ill it will make you if you eat it (mild illness, hospitalisation, death).
Risk assessment is the scientific/technical approach of estimating the risk.
The risk assessment approach can be broken into 4 steps which are:
- Identification of the hazard (type of contaminant and foodstuff it may be found in).
- Characterisation of the hazard (the response of individuals to an ingested dose and subsequent illness).
- Exposure assessment (estimating likely contamination rates from farm to fork and calculating the dose ingested per meal).
- Risk characterisation (working out risk across the exposed population determining number of illnesses, hospitalizations and deaths each year).
Together with the supervisors, the student will select the type of hazard to be studied (e.g. from list above) and the type of food (e.g. dairy, fish, meat, salads, complex foods etc.).
A quantitative risk assessment model will be built from farm to fork. Key variables at each step of the chain will be identified and data will be obtained from the literature and the supervisors own research.
The model can be implemented in various ways depending on the preference and skillset of the student. This ranges from using existing software implemented on excel to the possibility of developing the model in a high level language. All training will be provided and there may also be the opportunity to work alongside industry.
By the end of the project, the acquired skillset will make the student well placed for employment in risk assessment in not only the food sector but also other sectors that value similar expertise (e.g. environmental science, finance etc.).
Candidates should have (or expect to achieve) a UK honours degree at 2.1 or above (or equivalent) in Applied maths, food science, quantitative biology, bioinformatics, statistics, physics, data science, biology but with expertise in statistics and computing. A numerate background is not essential but should have an interest in food safety or risk assessment along with an interest in modelling of biological systems. Experience in modelling, computer programming and computational models is not essential but would be beneficial.
• Apply for Degree of Doctor of Philosophy in Physics
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form
When applying please ensure all required documents are attached:
• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV
• Details of 2 academic referees
Informal inquiries can be made to Prof Norval Strachan ([email protected]
) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ([email protected]
Bao Dominguez, M., Pierce, GJ., Pascual, S., González-Muñoz, M., Mattiucci, S., Mladineo, I., Cipriani, P., Bušelić, I. & Strachan, NJC. (2017). 'Assessing the risk of an emerging zoonosis of worldwide concern: anisakiasis'. Scientific Reports, vol 7, 43699. Available at www.nature.com/articles/srep43699
Poppy G (2016) Food Allergy and Intolerance. CSA Science Report available at www.food.gov.uk/sites/default/files/media/document/fifth-csa-report-allergy%20%281%29.pdf
FAO/WHO (2009) Principles and methods for the risk assessment of chemicals in food. Available at www.who.int/foodsafety/publications/chemical-food/en/
Eva Møller Nielsen, Jonas T. Björkman, Kristoffer Kiil, Kathie Grant, Tim Dallman, Anaïs Painset, Corinne Amar, Sophie Roussel, Laurent Guillier, Benjamin Félix, Ovidiu Rotariu, Francisco Perez‐Reche, Ken Forbes, Norval Strachan (2017) Closing gaps for performing a risk assessment on Listeria monocytogenes in ready‐to‐eat (RTE) foods: activity 3, the comparison of isolates from different compartments along the food chain, and from humans using whole genome sequencing (WGS) analysis. EFSA supporting publication. https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2017.EN-1151
Teunis, P. F., F. Kasuga, A. Fazil, I. D. Ogden, O. Rotariu, and N. J. Strachan. 2010. Dose-response modeling of Salmonella using outbreak data. Int. J. Food Microbiol. 144:243-249.
Rotariu, O., I. D. Ogden, L. MacRitchie, K. J. Forbes, P. Cross, A. P. Williams, C. J. Hunter, P. F. M. Teunis, and N. J. C. Strachan. 2012. Applying risk assessment and spatial epidemiology to elucidate the source of human E. coli O157 infection. Epidemiology and Infection, 140(8), 1414-1429.