The dynamics of drug resistance within hospital populations of Gram-negative bacteria

当细菌对抗生素产生耐药性时，它们引起的感染就更难治疗了。在英国和全球范围内，我们看到由抗生素耐药细菌引起的感染数量正在增加。这可能会把我们带回到20世纪初之前的抗生素时代，那时简单的伤口感染可能会危及生命，而抑制免疫系统的癌症治疗和依赖抗生素来预防感染将无法使用。抗生素耐药性是由于抗生素的使用和滥用而产生的，因此，即使我们开发出新的抗生素，我们也很可能永远面临耐药菌株的问题。我们需要考虑如何在不影响病人护理的情况下最好地保护我们现有的抗生素。要做到这一点，方法之一是看看为什么有些地方的阻力比其他地方小，并试图找出他们做得对的地方。例如，一些医院的耐药细菌感染比其他医院少。在这个项目中，我将探索这些差异的原因，并将这些发现转化为更好地防止耐药性传播的方法。为此，我将建立抗生素耐药性传播的数学模型。数学模型是一个框架，在这个框架中，不同的亚种群被分离出来，这些亚种群的增减率被计算出来。例如，一个模型会将医院的人群分为有耐药细菌或易感细菌感染的人群和没有感染的人群。然后，它会考虑人们感染的速度和机制，然后探索有抗药性和没有抗药性的人之间的区别。通过用数学方法写出这个框架，我们可以更好地了解耐药性传播的过程，并可以确定需要解决的关键目标——例如过度使用某种类型的抗生素。根据对耐药性传播的理解，该模型可用于预测未来在没有干预的情况下会发生什么，然后将其与引入某些干预措施时会发生的情况进行比较。我将建立数学模型，捕捉不同医院发生的情况，并确定为什么有些医院的耐药性比其他医院低。特别是，我将研究一组被称为革兰氏阴性菌的细菌的耐药性的发展。这些细菌通常生活在肠道中，但它们可以传播到身体的其他部位，在英国，它们是导致严重的医院相关感染（如菌血症（血液感染））的最常见原因。我们越来越多地看到这些细菌的菌株对常见的强效抗生素产生耐药性，因此它们是抗生素耐药性的关键因素。细菌群可以有非常不同的特征，可以生长得非常快。它们的基因组成非常灵活，这意味着新的基因变化可以发生，或者新的遗传物质片段可以在细菌之间跳跃，产生和传播耐药性。在一个细菌种群中，会有许多不同的菌株，这些菌株可能有许多不同的耐药性。这种多样性以前很少在数学模型中被考虑，所以我们可能错过了抗性进化的关键部分。在这个项目中，我将开发数学模型来整合这种多样性，并确定它对耐药性传播的影响有多大。数学模型必须以数据为基础，以便与临床医生和公共卫生相关。在这个项目中，我将使用新收集的医院层面的抗生素使用和耐药性数据，为我的模型获得参数，并确定模型应该捕捉哪些模式。我的研究结果将立即对临床医生和NHS有用，并将直接影响用于控制抗生素耐药性出现和传播的干预措施。

When bacteria become resistant to antibiotics the infections they cause are harder to treat. In the UK, and globally, we are seeing an increase in the number of infections being caused by antibiotic resistant bacteria. This could lead us back into the pre-antibiotic era before the early 1900s when infections of simple cuts may become life threatening and cancer treatments which suppress the immune system, and rely on antibiotics to prevent infections, will be unusable. Antibiotic resistance arises through the use and misuse of antibiotics and so, even if we develop new antibiotics, it is likely that we will always be faced with the problem of resistant strains. We need to consider how best to preserve our existing antibiotics without compromising patient care. One of the ways to do this is to look at why some places have less resistance than others and to try to work out what they are doing right. For example, some hospitals have fewer infections with resistant bacteria than others. In this project I will explore what the reasons for these differences might be and translate these findings into ways to better prevent resistance from spreading. To do this I will build mathematical models of antibiotic resistance spread. Mathematical models are frameworks in which different subpopulations are separated out and the rates at which these sub-groups increase or decrease are calculated. For example, a model would split a hospital population into those with and without infections with resistant or susceptible bacteria. It would then consider at what rate and by what mechanism people become infected, and then explore what the difference is between those with and without resistance. By writing this framework down mathematically, we get a better understanding of the processes underlying the spread of resistance and can identify the key targets - for example overuse of a certain type of antibiotic - that need to be tackled. From this understanding of the resistance spread, the model can be used to predict what will happen in the future without interventions and then compare this to what would happen if certain interventions were introduced. I will build mathematical models that capture what is happening in different hospitals and determine why some have lower rates of resistance than others. In particular, I will look at the development of resistance within a group of bacteria called the Gram-negatives. These bacteria are often found living in the gut, but they can travel to other parts of the body and, in the UK, are the most common cause of serious hospital-associated infections such as bacteraemia (infection of the blood). Increasingly we are seeing strains of these bacteria becoming resistant to common, powerful antibiotics and so they are a key contributor to antibiotic resistance. Groups of bacteria can have very different characteristics and can grow extremely rapidly. Their genetic make-up is very flexible which means that new genetic changes can occur or new pieces of genetic material can jump between bacteria creating and spreading resistance. In a bacterial population there will then be many different strains that may have many different resistances. This diversity has rarely been considered in mathematical models before, and so we may be missing a key part of resistance evolution. In this project I will develop mathematical models to incorporate this diversity and to determine how much of an impact it is having on resistance spread.Mathematical models must be grounded in data in order to be relevant to clinicians and public health. In this project I will use the newly collected hospital level data on antibiotic usage and resistance to both gain parameters for my models and to determine what patterns the models should capture. My results will then be immediately useful for clinicians and the NHS, and will directly influence the interventions used to control the appearance and spread of antibiotic resistance.