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

对结核病进行数学建模：使用肺部扫描来绘制感染图，并使用基于个体的混合模型来模拟感染和治疗

• 批准号: MR/Y010124/1
• 负责人: Ruth Bowness
• 金额: $ 255.27万
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY010124%2F1
• 关键词: Mathematically; modelling; tuberculosis; using; lung

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.

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液滴扩散时，它可以发展。过去，结核病或“消费”是全球死亡的主要原因。在发现抗生素和生活条件的总体改善之后，疾病的患病率下降了。但是，自1980年代以来，案件再次增加。 TB现在是最大的传染病杀手（艾滋病毒/艾滋病和现在的艾滋病）。该项目旨在迈出个性化结核病治疗方面的一步。目前，有针对结核病疾病的治疗，医生必须遵循严格的治疗方案，仅允许患者体重的变化。这些治疗方案是几年前定义的，当时对这种疾病的了解很少。现在，我们对结核病细菌以及感染动力学如何根据特定患者的免疫反应而改变。例如，患有糖尿病和/或HIV的人往往患有更复杂和严重的结核病疾病。我们还知道，感染的严重程度，即受影响的肺组织的量，在治疗的成功程度中起着作用。该项目旨在根据其细菌负担进行结核病患者，即出现了多少感染，以及可能使他们的结核病疾病更加复杂的任何其他疾病（例如糖尿病或HIV）的存在，以找到最佳的治疗方式。该算法将能够确定感染出现的肺部以及其严重程度。这将意味着将来的结核病医生可以将单个结核病患者的肺部扫描并馈入AI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，我将使用数学建模来模拟该患者肺部会发生什么（还考虑了他们的特殊免疫反应，通过考虑糖尿病或HIV阳性）。我已经开发了数学模型，能够在结核病感染过程中模拟典型的免疫反应，并将与相关的生物学家合作，在患者也是糖尿病/HIV阳性的感染动力学中所看到的差异的整合。这种类型的数学模型的建造数学模型很复杂，并且与我的生物相关的过程中有很多不知情的过程，这是我与最新的生物相关的过程，以确保与生物学相关的过程。我还将与数学/计算同事一起使用相关技术来帮助模型开发，并测试模型的准确性。我还将使用南非临床试验中的其他数据来测试模型预测。一旦我相信AI算法和模型是强大的，我将与医生合作，试图找到更多特定于患者的治疗方案。这将来意味着一些患者不需要太多的治疗（因此降低成本并减少这些患者的副作用），有些患者需要在目前开处方的抗生素组合/剂量的变化。最终，这将有助于增加治疗成功，防止未来的结核病复发，并减少出现抗生素抗性的机会。