Mathematical modelling to project the impact of public health interventions targeted to individuals previously treated for tuberculosis on the trajectory of the tuberculosis epidemic and the emergence of drug-resistant diseae in Southern Africa

结核病(TB)是一种由结核分枝杆菌引起的空气传播传染病，是全球公共卫生面临的重大挑战。世界卫生组织估计，2012年，全球有860万人患上了结核病，130万人死于这种疾病。南部非洲仍然是全球结核病流行的中心，那里的国家在过去20年中经历了前所未有的结核病病例增加，这与艾滋病毒/艾滋病的巨大负担有关。业务性研究表明，在许多情况下，传统的结核病控制方案依赖于被动发现病例和治疗主动疾病，目前无法大幅减少结核病的流行和传播。因此，正在调查更积极的公共卫生干预措施，例如全人群结核病筛查和异烟肼预防性治疗。鉴于资源有限，而且迄今为止无针对性的人群干预试验的结果令人失望，使用更有针对性的努力来改善结核病控制可能是一个有希望的替代方案。可考虑进行有针对性干预的一个可识别的风险群体是有既往治疗结核病病史的个人。在一些高负担环境中，这些疾病占结核病负担的三分之一以上，因此可能对继续传播有很大贡献。此外，以前接受治疗的个人也可能对耐药结核病的传播做出不成比例的贡献。目前尚不清楚旨在预防或及早发现以前治疗过的人中复发结核病的干预措施是有效的还是具有成本效益的干预措施。该项目的主要目标是将传统的流行病学分析和数学建模相结合，以更好地了解在当前南部非洲结核病和艾滋病毒流行的背景下，以前治疗过的结核病的重要性，并预测有针对性的控制干预措施的影响。我们将使用ZAMSTAR研究的大量现场数据来源，ZAMSTAR研究是在赞比亚和南非的24个社区进行的一项大型随机干预试验，以研究以前在高负担环境中治疗的结核病。我们的进一步目标是开发一种新的、基于人群的结核病流行的动态分区数学模型，该模型包含了人群中以前治疗过的结核病、艾滋病毒合并感染和耐药结核病的历史。该模型将根据来自9个非洲国家的结核病数据进行校准。我们的目标是利用该模型预测针对以前接受治疗的个人的各种控制干预措施对南部非洲不同环境中结核病流行轨迹和出现抗药性的影响。该研究项目力求对目前关于结核病和艾滋病毒流行的复杂动态的认识作出重要贡献，并为在南部非洲和其他地方制定结核病和耐药结核病控制的新工具和战略提供信息。

Tuberculosis (TB), an air-borne infectious disease caused by Mycobacterium tuberculosis, is a major challenge to global public health. The World Health Organization estimates that in 2012, globally 8.6 million people developed TB and 1.3 million people died from the disease. Southern Africa remains the center of the global TB epidemic, where countries have experienced an unprecedented increase in TB cases over the past two decades, associated with an enormous burden of HIV/AIDS. Operational research suggests that in many settings, conventional TB control programs, which rely on passive case finding and treatment of active disease, are currently unable to substantially reduce the prevalence of TB and transmission. More aggressive public health interventions, for example population-wide screening for TB and isoniazid preventive therapy, are therefore being investigated. Given limited resources and thus far disappointing results from untargeted population-level intervention trials, the use of more targeted efforts to improve TB control may be a promising alternative. One identifiable risk group that might be considered for targeted interventions are individuals with a history of previously treated TB. These account for more than one-third of the TB burden in some high-burden settings, and thus might contribute substantially to onward transmission. Moreover, previously treated individuals may also disproportionately contribute to the spread of drug-resistant forms of TB. It is currently not known whether interventions aiming to prevent or early identify recurrent TB in previously treated individuals would be an effective or cost-effective intervention.The main goal of this project is to combine traditional epidemiological analysis and mathematical modeling to better understand the importance of previously treated TB in the context of the current TB and HIV epidemic in Southern Africa, and to project the impact of targeted control interventions. We will use an extensive source of field data from the ZAMSTAR study, a major randomized intervention trial conducted in 24 communities in Zambia and South Africa, to study previously treated tuberculosis in high-burden settings. We further aim to develop a novel, population-based dynamic compartmental mathematical model of the TB epidemic that incorporates history of previously treated TB, HIV co-infection and drug-resistant TB in the population. The model will be calibrated to TB data from 9 African countries. We aim to use the model to project the impact of various control interventions targeted to previously treated individuals on the trajectory of the TB epidemic and the emergence of drug-resistance in different settings in Southern Africa. The research project seeks to make an important contribution to current knowledge about the complex dynamics of TB and HIV epidemics and to inform the development of novel tools and strategies for TB and drug-resistant TB control in Southern Africa and elsewhere.