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

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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.