Poject Highlights:

  • Big picture AMR dynamics in the environment from sewage to rivers
  • Cross-disciplinary skill set
  • Communication with stakeholders and public outreach welcome

The widespread use of antibiotics in humans and animals and as growth promoters for livestock has not only selected for resistance genes but also for plasmids carrying them. This has led to a growing global concern for the spread of antimicrobial resistance (AMR), which would have enormous public health implications for nations of all income and development levels. This project would enable the student to gain inter-disciplinary training and mentoring to tackle a largely unknown but critical link in the spread of AMR, namely if and to what extent AMR can persist in our river networks. Using the Thames river in the UK as a test case, this project will provide the first assessment of how and when AMR can enter, persist, and be transported, within a large rural to urban river system.

Resistance genes can enter the environment via hotspots such as hospital sewers, wastewater treatment plants and animal manures and slurries on farms. Runoff from these fields and effluent from wastewater treatment enter rivers and can there interact with river sediments. In addition, large rainfall and runoff events can overwhelm storm drainage and wastewater treatment systems, allowing untreated sewage to enter river systems directly.

Building on previous work, this project will develop a mechanistic mathematical model to better understand resistance selection and transport from where it enters the river network, within the river itself, and its downstream transport. Importantly, this model will be placed in context of the hydrological functioning of the Thames catchment, and also incorporate the large metagenomic sequencing and antibiotic concentration datasets available throughout the catchment.

Overview of AMR sources and transport in the environment. The proposal will focus on the water cycle. Environment Agency (EA) monitoring in red (Water Framework Directive: WFD). From Singer et al. (2016).


The mathematical model will be based on ordinary differential equations and potentially include an agent-based sub-model to capture the nested organization of resistance genes on plasmids in host bacteria. We are using this approach to extend the Activated Sludge Model 1 with resistance plasmid transmission, antibiotic turnover and including enteric bacteria from faeces to model exchange of plasmids between enteric and indigenous wastewater bacteria. We are currently analysing the one compartment model (manuscript in preparation). In the project, we will develop the multi-compartment model and use Approximate Bayesian Computation (ABC) statistical model selection and inference methodology.

Training and Skills

This project is highly interdisciplinary, and will provide the student with a unique quantitative skill set in environmental and microbial dynamics. The Doctoral Researcher (DR) will acquire a broad set of mathematical modelling, statistical data analysis and programming skills.

This will be enhanced by interdisciplinary collaboration with the Larsen, Wellington, Singer and Quince groups as the DR will learn to communicate and collaborate with experimental researchers and scientists from many disciplines. The modelling will also guide future experimental effort by identifying the most important parameters and processes.

Moreover, the DR will have the opportunity for public outreach activities to inform the AMR debate with the results of our project. Project management and communication skills will also be gained.


Year 1:

Learning to build and building a multi-compartment AMR dynamics and transport model (Paper 1). This will extend an existing single-compartment model to include upstream and downstream compartments to create a complete model of AMR transmission on the scale of a catchment. Learn about and start using published data and data generated by the JPIAMR and the Thames catchment project, in collaboration with another CENTA DR, James Delaney, supervised by Wellington & Quince, who is working with this dataset.

Year 2:

Develop Bayesian model selection and parameter inference methods to fit the mechanistic model built in year 1 to the datasets mentioned above (Paper 2). We aim to develop a robust statistical pipeline for selection of mechanistic mathematical models describing the underlying processes in AMR transmission rather than just fitting a statistical model to the outcome of these processes.

Year 3:

Simulate the effect of various mitigation strategies on AMR transmission (Paper 3).

If time remains, use the model and its inferred parameters to quantify the risk of resistance transmission in different compartments and overall (Paper 4).

Partners and collaboration (including CASE)

This project provides a unique opportunity for the student to work closely with a number of collaborators across different disciplines. Firstly, at the University of Birmingham with Jan Kreft, whose group has >15 years’ experience in mathematical modelling, including modelling plasmid dynamics and AMR dynamics in wastewater treatment. Secondly, with Josh Larsen, whose group has extensive experience in catchment hydrological processes, monitoring, and modelling.

At the University of Warwick, the Wellington lab has for many years driven forward the research on AMR dynamics in the Thames catchment with collaborators from CEH, Thames Water and others.

The Quince group is at the forefront of developing more rigorous statistical and bioinformatic algorithms e.g. for reconstructing genomes from metagenomes.

Further Details

This project has been selected as a CENTA Flagship project. This is based on the projects fulfilment of specific characteristics e.g., NERC CASE support, collaboration with our CENTA high-level end-users, diversity of the supervisory team, career development of the supervisory team, collaboration with one of our Research Centre Partners (BGS, CEH, NCEO, NCAS), or a potential applicant co-development of the project.


Dr Jan-Ulrich Kreft

School of Biosciences & Institute of Microbiology and Infection & Centre for Computational Biology

The University of Birmingham

Edgbaston, Birmingham, B15 2TT, UK

Tel: +44 (0)121 41-48851

Email: j.kreft@bham.ac.uk

Web: www.tinyurl.com/kreftlab


Dr Joshua Larsen

School of Geography, Earth and Environmental Sciences

The University of Birmingham

Edgbaston, Birmingham, B15 2TT, UK

Email: J.Larsen@bham.ac.uk

Web: https://www.birmingham.ac.uk/staff/profiles/gees/larsen-joshua.aspx


Professor E M H Wellington

School of Life Sciences

The University of Warwick

Coventry CV4 7AL, UK

Tel: +44 (0)2476 523184

Email: e.m.h.wellington@warwick.ac.uk

Web: http://www2.warwick.ac.uk/fac/sci/lifesci/people/ewellington


Dr Christopher Quince

Warwick Medical School – Microbiology and Infection

The University of Warwick

Coventry CV4 7AL, UK

Tel: +44 (0)2476 522317

Email: C.Quince@warwick.ac.uk

Web: https://warwick.ac.uk/fac/sci/med/staff/cquince/