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飞沫传播时，就会出现这种情况。过去，结核病或“肺痨”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，这种疾病的患病率下降了。然而，自 20 世纪 80 年代以来，病例数再次上升。结核病现在是最大的传染病杀手（高于艾滋病毒/艾滋病和现在的 COVID-19）。该项目旨在在个性化结核病治疗方面向前迈出一步。目前，在治疗结核病时，医生必须遵循严格的治疗方案，只允许患者体重发生变化。这些治疗方案是多年前制定的，当时人们对这种疾病知之甚少。我们现在对结核菌以及感染动态如何根据特定患者的免疫反应而变化有了更多的了解。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，对治疗的成功程度起着一定的作用。该项目旨在根据结核病患者的细菌负荷（即存在多少感染，以及是否存在可能使结核病变得更加复杂的任何其他病症（例如糖尿病或艾滋病毒））对结核病患者进行分组，以便找到治疗结核病的最佳方法。我将使用南非一项临床试验中收集的肺部扫描数据来开发人工智能 (AI) 算法，以自动识别患者的结核病感染情况。该算法将能够识别感染出现在肺部的位置以及感染的严重程度。这意味着未来结核病医生可以对单个结核病患者进行肺部扫描，并将其输入人工智能算法，以自动将该患者的结核病感染情况映射到计算机上。一旦进入计算机，我将使用数学模型来模拟患者肺部会发生什么（同时考虑他们的特定免疫反应，考虑他们是否患有糖尿病或艾滋病毒阳性）。我已经开发了数学模型，能够模拟结核病感染期间的典型免疫反应，并将与相关生物学家合作，整合当患者同时患有糖尿病/艾滋病毒阳性时感染动态的差异。建立这种类型的数学模型很复杂还有很多未知因素，这就是为什么我将与我的生物合作者密切合作，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。我还将使用南非临床试验的额外数据来测试模型预测。一旦我确信人工智能算法和模型是稳健的，我将与医生合作，尝试找到更多针对患者的治疗方案。这意味着将来一些患者将不需要那么多的治疗（从而降低这些患者的成本并减少副作用），而有些患者则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，并减少出现抗生素耐药性的机会。