Mathematically modelling tuberculosis: using lung scans to map infection, and a hybrid individual-based model to simulate infection and treatment

结核病（TB）是一种通常影响肺部的传染病。当细菌通过空气中的飞沫传播时，它会发展。在过去，结核病或“肺结核”是全世界死亡的主要原因。在抗生素的发现和生活条件的普遍改善之后，这种疾病的流行率下降了。然而，自1980年代以来，这类案件又有所增加。结核病现在是最大的传染病杀手（超过艾滋病毒/艾滋病和现在的COVID-19）。该项目旨在向结核病个性化治疗迈出一步。目前，在结核病的治疗中，医生必须遵循严格的治疗方案，只允许患者体重的变化。这些治疗方案是几年前定义的，当时对这种疾病了解甚少。我们现在对结核菌有了更多的了解，以及感染动力学如何根据特定患者的免疫反应而变化。例如，患有糖尿病和/或艾滋病毒的人往往患有更复杂和更严重的结核病。我们还知道感染的严重程度，即受影响的肺组织的数量，在治疗的成功程度中起着重要作用。该项目旨在根据结核病患者的细菌负荷，即感染程度以及是否存在任何其他情况，对结核病患者进行分组（如糖尿病或艾滋病毒），这可能使他们的结核病更加复杂，为了找到治疗它们的最佳方法。我将使用从南非临床试验中获得的肺部扫描数据集来开发人工智能（AI）自动识别患者结核病感染的算法。该算法将能够识别肺部感染的位置以及感染的严重程度。这将意味着，在未来，结核病医生可以对单个结核病患者的肺部进行扫描，并将其输入人工智能算法，以自动将患者的结核病感染映射到计算机上。一旦在计算机上，我将使用数学模型来模拟患者肺部会发生什么（还考虑到他们的特殊免疫反应，通过考虑他们是糖尿病患者还是HIV阳性）。我已经开发了能够模拟结核病感染期间典型免疫反应的数学模型，并将与相关生物学家合作，整合患者同时患有糖尿病/艾滋病病毒阳性时感染动力学的差异。构建这种类型的数学模型是复杂的，有许多未知数，这就是为什么我将与我的生物学合作者密切合作，以确保使用最新的实验室数据来量化所涉及的过程。我还将与数学/计算同事合作，使用相关技术来帮助模型开发，并测试模型的准确性。我还将使用来自南非临床试验的其他数据来测试模型预测。一旦我相信人工智能算法和模型是强大的，我将与医生合作，试图找到更多针对患者的治疗方案。这将意味着在未来，一些患者将不需要那么多的治疗（因此降低成本并减少这些患者的副作用），有些患者将需要改变目前处方的抗生素组合/剂量。最终，这将有助于提高治疗成功率，防止未来结核病复发，并减少抗生素耐药性出现的机会。

Tuberculosis (TB) is an infectious disease that usually affects the lungs. It can develop when bacteria spread through droplets in the air. In the past TB or "consumption" was a major cause of death worldwide. After the discovery of antibiotics and general improvement in living conditions, prevalence of the disease fell. However, since the 1980s cases have been rising again. TB is now the biggest infectious disease killer (above HIV/AIDS and now COVID-19). This project seeks to take a step forward in personalising TB treatment. Currently with treatment for TB disease, doctors must follow rigid treatment protocols that only allow for variations in patients' weights. These treatment regimens were defined years ago when very little was understood about this disease. We now know more about TB bacteria and how the infection dynamics can change depending on particular patients' immune responses. For example, people who have diabetes and/or HIV tend to have more complex and severe TB disease. We also know that the severity of infection, i.e. the amount of lung tissue affected, plays a part in how successful treatment will be. This project seeks to group TB patients according to their bacterial burden, i.e. how much infection is present, and the presence of any other conditions (such as diabetes or HIV) that could make their TB disease more complex, in order to find optimal ways of treating them.I will use a collection of lung scans taken from a clinical trial in South Africa to develop Artificial Intelligence (AI) algorithms to automatically identify TB infection in patients. This algorithm will be able to identify where in the lungs the infection appears and how severe it is. This will mean that in future TB doctors could take an individual TB patient's lung scan and feed it into the AI algorithm to automatically map that patient's TB infection onto a computer. Once on the computer, I will use mathematical modelling to simulate what would happen in that patient's lungs (also taking into account their particular immune response, by factoring in whether they are diabetic or HIV-positive). I have already developed mathematical models that are capable of simulating a typical immune response during TB infection and will work with relevant biologists to integrate the differences seen in infection dynamics when patients are also diabetic/HIV-positive.Building mathematical models of this type is complex and there are many unknowns, this is why I will work closely with my biological collaborators to ensure that the latest laboratory data is used to quantify the processes involved. I will also work with mathematical/computational colleagues to use relevant techniques to help with model development, and to test how accurate the models are. I will also use additional data from the South African clinical trial to test model predictions. Once I am confident that the AI algorithms and models are robust, I will work with doctors to try to find more patient-specific treatment protocols. This will mean in future that some patients won't need as much treatment (hence cutting costs and reducing side-effects for these patients), and some will need variations in the antibiotic combinations/doses that are currently prescribed. Ultimately this will help to increase treatment success, prevent future TB relapses, and reduce the chance of antibiotic resistance emerging.