Methods to Estimate the Effect of Interventions on the Incidence and Transmission of Tuberculosis

结核病（TB）是全球传染病死亡的主要原因，最近超过了艾滋病毒/艾滋病。 世卫组织已经为最终消除结核病设定了目标，但几乎没有可靠的监测工具 朝着这个目标前进。此外，我们对结核病的空间和人口变异性的理解 传播和发病率不足，使有限资源的分配面临挑战。这个项目 建议开发利用数据源真实的估计结核病传播和发病率的方法， 都是随处可见的在目标1中，我们将改进疾病的连续间隔和持续时间的估计 在诊断时使用现有数据（我们的和已发表的）。这些估计将利用统计数据， 方法和新的修改这些方法，使他们相关的结核病。这些估计数提供了 结核病监测的基础，进一步开展建模工作以指导政策，并提供有关结核病的宝贵见解 流行病学目标2将侧重于开发真实的估计结核病繁殖数量的方法， 提供传播模式的信息我们建议修改我们和其他人已经开发的方法， 依赖于常规收集的监测数据，我们将把这些 方法来估计异质性的生殖数量空间，跨人口统计学特征， 并根据耐药性的存在/不存在。在目标3中，我们解决了估计 发病率目前的估计数是在全国范围内提出的，并来自新的 个别国家向世卫组织提供的确诊个人。此数据不提供 发生率估计，并受到大量报告问题的阻碍，这些问题尚未得到很好的理解。我们 提出了一种新的贝叶斯方法来估计结核病发病率从结核病流行率调查，使用我们的 目标1中得出的持续时间估计值。结核病流行率调查是详细的整群抽样调查， 所有参与者的空间、临床和人口统计信息。它们在各个地区进行， 结核病的高负担。这种方法将提供有关结核病发病率及其 这是一种多样性，可以更有针对性地分配资源。我们将创建R包来传播 我们开发的所有方法都是为了广泛使用。这项工作将使决策者能够更好地 了解结核病的发病率以及疾病负担最重的群体和地区。 这些知识将指导干预工作，最终实现消除的目标。

Tuberculosis (TB) is the leading cause of infectious disease deaths globally, recently surpassing HIV/AIDS. The WHO has set goals for the eventual elimination of TB, but there are few, if any, reliable tools to monitor progress toward this goal. Additionally our understanding of the spatial and demographic variability of TB transmission and incidence is inadequate, making the allocation of limited resources challenging. This project proposes to develop methods to estimate transmission and incidence of TB in real time using data sources that are widely available. In Aim 1, we will develop improved estimates of the serial interval and duration of disease at time of diagnosis by using existing data (ours and published). These estimates will make use of statistical methods and novel modifications of these approaches to make them relevant to TB. These estimates provide the foundation of monitoring TB, further modeling work to guide policy, and provide valuable insights into TB epidemiology. Aim 2 will focus on developing methods to estimate the reproductive number of TB in real time, providing insight on transmission patterns. We propose to modify methods we and others have developed for diseases with short latency to TB, relying on routinely collected surveillance data. We will extend these methods to estimate heterogeneities in the reproductive number spatially, across demographic characteristics, and according the presence/absence of drug resistance. In Aim 3 we address the challenge of estimating disease incidence. Current estimates are given on a countrywide basis and result from annual reports of newly diagnosed individuals supplied to the WHO by individual countries. This data does not provide granularity in incidence estimates and is hampered by substantial reporting issues, which are not well-understood. We propose a novel Bayesian approach to estimate TB incidence from TB prevalence surveys, using our estimates of duration derived in Aim 1. TB prevalence surveys are detailed cluster sampled surveys with spatial, clinical, and demographic information on all participants. They are performed throughout areas with a high burden of TB. This approach will provide more detailed information on TB incidence and its heterogeneities, allowing for more targeted allocation of resources. We will create R packages to disseminate the methods that we develop in all the aims for widespread use. This work will enable policy makers to better understand the incidence of TB as well as groups and areas that are carrying the highest burden of disease. This knowledge will guide intervention efforts, with the eventual goal of elimination.