High-dimensional inference for models of antimicrobial resistance transmission in open populations.

点击翻译按钮获取中文摘要

Antimicrobially resistant (AMR) bacteria are those for which antibiotics are significantly less effective than expected. The spread of AMR bacteria is a major and increasing global public health concern, as the introduction of any new antibiotic eventually leads to the spread of resistant bacteria. We will be mathematically modelling the transmission of AMR bacteria through open populations, and using data to learn about the underlying properties of the bacteria (e.g., rate of transmission). Performing this kind of inference is often complicated by incomplete data - we do not always know when an individual has been infected (only when they develop symptoms), and test results are not always 100% accurate.This project is part of an investigation into the spread of AMR bacteria in the North West of England, which will be using data collected from hospitals, care homes, and across the community (since people in care settings are at particular risk of AMR-related infection). In order to explain the transmission of AMR bacteria through open populations, we will be developing novel models and inference methods, which we can use to analyse the data. We will start by developing methods for small-scale settings, such as hospitals and care homes, then develop methods for open settings, such as the wider Liverpool area.Industrial partner: Liverpool School of Tropical Medicine, Unilever.