MRC Transition Support: A mathematical modelling framework for tuberculosis burden estimation and economic evaluation of pharmaceutical interventions.

结核病是全球疾病和死亡的主要原因，每年约有1000万人患病，约1.2人死亡(不包括艾滋病毒感染者中约20万人死于结核病)。在过去的20年里，全球结核病发病率只以每年几个百分点的速度下降。大约有50万人患上了对利福平耐药的结核病，利福平是治疗结核病的关键药物之一。正在开发治疗结核病的新药和药物组合，以及可能预防成人疾病的新疫苗。量化结核病的负担对于了解其全球流行病学和做出适当的资源分配决定是至关重要的。对每年新增结核病病例数量的大多数估计在很大程度上依赖于各国在该年向世卫组织报告的病例数量。不幸的是，三分之一的结核病病例被认为要么没有发现，要么没有报告，因此报告的病例数量低估了新病例的数量。虽然人们可以纠正这一点，但很难确切知道报告的数字应该调整多少。即使诊断出结核病，耐药性也经常被漏掉，导致不适当的治疗，可能会让耐药菌株传播给其他人，并允许结核病菌株对更多药物产生耐药性。这种复杂性，再加上低识别率，使得估计耐药结核病的负担，以及理解由传播而不是获得性治疗耐药在多大程度上驱动耐药变得更具挑战性。这项工作的一个主要目标是使用数学传播模型来估计负担，并为所有数据提供一个统一的框架。这些模型产生了新病例、死亡人数以及疾病流行率。它们可以明确地代表疾病传播(包括抗药性菌株)，因此引入了不同年份新病例数量之间的相关性。这些模型涉及从以前的流行病学工作中得出的参数，但必须进行校准，以从关于结核病报告、死亡和流行率的数据中了解情况。校准意味着调整不完全已知的模型参数，以便使观测模型输出与数据相匹配。这个过程提供了一个模型，可以用来预测负担，但也可以教会我们一些关于潜在过程的东西。与结核病流行病学和病程有关的许多参数都很不确定，而这种不确定性很少在模型中得到充分体现。

Tuberculosis (TB) is a major cause of disease and death globally, sickening around 10 million people and killing around 1.2 people annually (excluding the 200,000 or so deaths due to TB in people with HIV infections). Global TB rates have only been declining at a couple of percent per year for the last two decades. Around 500,000 people developed TB that was resistant to rifampicin - one of the key drugs used to treat TB. New drugs, and combinations of drugs, are being developed to treat tuberculosis, as are new vaccines that may protect against disease in adults.Quantifying the burden of TB is fundamental to understanding its global epidemiology and for making appropriate resource allocation decisions. Most estimates of new TB case numbers each year rely strongly on the number of cases reported by countries in that year to WHO. Unfortunately, one in three TB cases are thought to go either undetected or unreported, so the number of cases reported underestimates the number of new cases. While one can correct for this, it is hard to know exactly how much to adjust the reported numbers. Even if TB is diagnosed, drug resistance is often missed, leading to inappropriate treatment that can let drug-resistant strains to transmit to others, as well as allowing TB strains to acquire resistance to additional drugs. This complexity, combined with the low rate of identification, makes estimating the burden of drug-resistant TB, and understanding how much drug resistance is driven by transmission as opposed to acquired resistance on treatment, even more challenging.A major goal of this work is to use mathematical transmission models for burden estimation and provide a unified framework for all data. These models produce the number of new cases, deaths, and also the prevalence of disease. They can explicitly represent disease transmission (including drug-resistant strains) and so introduce a dependence between the number of new cases in different years. These models involve parameters derived from previous epidemiological work, but must be calibrated to learn from data on TB reports, deaths and prevalence. Calibration means adjusting imperfectly known model parameters in order to match observed model outputs to the data. This process provides a model that may be used to make predictions about burden, but may also teach us something about the underlying processes. Many of the parameters concerning the epidemiology and disease course of TB are quite uncertain, and this uncertainty is rarely represented fully in models.