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

抗菌素耐药(AMR)细菌是指抗生素的疗效明显低于预期的细菌。AMR细菌的传播是一个重大的且日益严重的全球公共卫生问题，因为任何新抗生素的引入最终都会导致耐药细菌的传播。我们将对AMR细菌通过开放种群的传播进行数学建模，并使用数据来了解细菌的基本属性(例如，传播率)。进行这种推断往往因为数据不完整而变得复杂--我们并不总是知道一个人什么时候被感染(只有当他们出现症状时)，而且测试结果并不总是100%准确。这个项目是对AMR细菌在英格兰西北部传播的调查的一部分，该调查将使用从医院、疗养院和整个社区收集的数据(因为在护理环境中的人特别容易受到AMR相关感染的风险)。为了解释AMR细菌通过开放种群的传播，我们将开发新的模型和推理方法，我们可以用它们来分析数据。我们将首先为医院和疗养院等小规模环境开发方法，然后为更广泛的利物浦地区等开放环境开发方法。工业合作伙伴：联合利华利物浦热带医学院。

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.