We are looking to recruit a PhD student with a strong background in statistics or mathematics to this fully funded 3 year PhD studentship to investigate transmission of drug resistant bacteria across health and social care settings in Liverpool.
Antimicrobial resistance is a significant threat to global public health. The introduction of any new antibacterial agent has invariably been followed by the ability of bacteria to inactivate or evade it, often with subsequent global dissemination of resistant bacterial strains. This has certainly been the case for the common human pathogens Escherichia coli and Klebsiella pneumoniae, which can increasingly produce enzymes (extended-spectrum beta lactamases (ESBL) and carbapenemases) which inactivate the antimicrobials usually used to treat them, and can prove particularly challenging to treat.
These bacteria are spread primarily by the faecal-oral route and so, in principle, infection prevention and control (IPC) interventions could block their spread - but despite this all efforts to contain them have failed and they have disseminated globally. Part of the difficulty is that at the individual level, routes of transmission are largely unknown. Classical epidemiology has identified that health and social care facilities like nursing and residential homes are associated with acquisition of ESBL and carbapenemase producing E. coli and Klebsiella pneumoniae. This PhD therefore aims to address this knowledge gap, to use cutting edge transmission models to infer transmission routes of these organisms in health and social care facilities in Liverpool.
The successful candidate will be nested in the TRACS-Liverpool (TRacking Antimicrobial resistance across Care Settings-Liverpool) project, a genomic surveillance study based in care homes and hospitals in Liverpool, part of LSTM’s iiCON (infection innovation) consortium, and a collaboration with Unilver. The study combines clinicians, social scientists, microbiologists, health systems researchers and mathematicians, aiming to find ways to reduce the spread of antimicrobial resistance in these facilities, using whole-genome and metagenomic sequencing techniques as a high-resolution typing tool to track bacteria within and between people across care settings. Your job, supported by the supervisory team, will be to build and fit transmission models incorporating genomic data to infer transmission routes, using Monte Carol simulation and computationally intensive Bayesian inference methods. These findings will inform interventions to block transmission, which will be tested in interventional studies.
This project would suit a candidate with a statistics or mathematical background, or previous experience of transmission modelling. Previous experience of bioinformatic analyses would beneficial.