Antimicrobial resistance (AMR) is one of the most serious and rapidly growing public health threats in the world today and already accounts for hundreds of thousands of deaths each year. Widespread use of antibiotics in animal production systems is a significant concern for human health because of the associated risk of resistance and the fact that virtually all classes of antimicrobials important for human medicine are also used in animals. The use of antimicrobials and the generation of AMR on farms may lead to the transmission of AMR organisms and genetic elements coding for resistance to humans. Transmission mechanisms include direct contact with animals, consumption of foods of animal origin and dissemination through animal waste.
The problem is particularly severe in many developing countries in Southeast Asia where regulation and biocontainment measures are limited and levels of antibiotic use are high. For example, one recent study reported that antimicrobial usage in household chicken farms in the Mekong delta region is approximately six times greater than reported in some European countries.
However, there are fundamental knowledge gaps in our understanding of precisely how antibiotic use in farming systems determines levels of resistance in animals and humans, and simple models suggest that not just total volume of antibiotic use but spatial and temporal patterns of antibiotic use can be important determinants of the emergence, spread and maintenance of resistance. Moreover, to make meaningful predictions of the impact of interventions to reduce unnecessary use of antibiotics in farming systems requires a quantitative and mechanistic understanding of the dynamic processes by which (1) antibiotic use selects for resistance in these systems and (2) antibiotic resistance determinants are transferred across the different compartments (transmission). Such understanding is currently lacking.
This DPhil project, which will be based at either the Oxford research unit in Thailand (Mahidol Oxford Tropical Medicine Research Unit, MORU) or in Vietnam (Oxford University Clinical Research Unit, OUCRU), aims to address some of these knowledge gaps by using rich local data sources to inform the development of mechanistic mathematical models for the spread of AMR in poultry production systems in Southeast Asia. The ultimate aim is to develop predictive models of how levels of antibiotic-resistance change in response to changing both usage levels of specific antibiotics and patterns of antibiotic usage. Models will be used to inform the development of interventions to reduce the AMR burden in the region.
We will provide a comprehensive training programme in advanced mathematical and statistical modelling techniques.
The research will be will be highly collaborative in nature and is likely to involve interactions with veterinarians, microbiologists, pharmacists, modellers, statisticians and epidemiologists.
The project also benefits from extensive collaborative links both with other Oxford researchers and departments and internationally. Students will be expected to publish their work, present findings at international meetings, attend local and external training courses, and attend weekly group meetings, journal clubs and departmental seminars.
This DPhil project would be suitable for a highly motivated student with excellent quantitative skills and a passion for understanding complex biological systems.
Project reference number: 968
|Professor Ben Cooper||Tropical Medicine||Oxford University, Bangkok||THA||Ben@tropmedres.ac|
|Dr Hannah Clapham||Tropical Medicine||Oxford University, Ho Chi Minh City||VNMfirstname.lastname@example.org|
|Dr Juan Carrique-Mas||Tropical Medicine||Oxford University, Ho Chi Minh City||VNM||jCarrique-Mas@oucru.org|
There are no publications listed for this DPhil project.