Quantifying HIV Transmission Risk in Sex/Drug Networks

描述（由申请人提供）：由于传染病是 我们对疾病传播的理解 和预防都植根于人口传播动力学理论。的 艾滋病毒等性传播感染（STI）的流行病学-- 它们的传播和谁被感染--是由人与人之间的网络驱动的 联系人.早期的流行病学研究和这一过程的数学模型 提供了一些见解，导致了STI控制战略的变化 在20世纪80年代然而，随着艾滋病毒的出现，出现了新的挑战。 像其他无法治愈的感染一样，艾滋病毒有可能广泛传播 在适当的环境下，这使得“核心集团” 20世纪80年代的概念对艾滋病毒预防的效果稍差。许多工作 在过去的15年里，已经做了一些工作，以确定 伙伴关系网络结构对艾滋病毒的传播至关重要， on partnership伙伴networks网络in many许多populations人口.模拟研究起到了 在这一努力中的关键作用，通过确定网络结构的类型， 对传输动态有很大影响。数据理论 和方法已经创建了一个明确的议程，量化的影响， 艾滋病毒传播风险的网络。虽然许多新兴的碎片 研究计划现在已经到位，网络之间存在着巨大的鸿沟。 数据和当前的模拟建模框架。模拟通常 间接地创建网络效应，通过改变一些方便的 函数以在模拟网络中产生变化。可观察网络 因此，衡量标准是模型的结果，而不是投入。虽然这 战略对于引导初步研究非常有用，它阻碍了 我们评估观察网络中经验传输风险的能力。 我们在这里提出了一个解决方案，该解决方案基于随机的统计模型 图：它可以用来从数据中估计网络参数，然后 模拟具有这些特性的网络。具体而言，我们建议：（1） 开发随机图模型，用于估计网络参数和仿真 不断发展的网络，具有流行病学相关的正式测试， 拟合优度估计和模拟方法都将基于 通用马尔可夫链蒙特卡罗（MCMC）算法，这将使研究人员 首次模拟具有相同统计特性的网络， 在真实的数据中观察到的那些性质。(2)使用这些方法来识别 网络结构对艾滋病毒的传播最重要。在 特别是，我们将研究针头共享的独立和联合影响， 和性传播，我们将测试是否混合， 并发性决定了网络中传输潜力的大部分。的 开发的方法将提供系统的经验基础， 注重伙伴关系干预的个人预防战略。 它将使公共卫生专业人员能够确定人口水平 预防战略，使网络不易受到传播。此外，委员会认为， 它将确定为这些努力提供信息所需的网络数据类型。

DESCRIPTION (provided by applicant): Because infectious diseases are transmitted from person to person, our understanding of disease transmission and prevention are rooted in a theory of population transmission dynamics. The epidemiology of sexually transmitted infections (STI) like HIV -- how quickly they spread and who is infected -- is driven by the network of person-to-person contacts. Early epidemiological studies and mathematical models of this process provided a number of insights that led to changes in STI control strategies during the 1980s. With the advent of HIV, however, new challenges have emerged. Like other incurable infections, HIV has the potential to spread very broadly in a population under the right circumstances. This makes the "core group" concept from the 1980s somewhat less effective for HIV prevention. Much work has been done during the last 15 years to identify which aspects of the partnership network structure matter for the spread of HIV, and to collect data on partnership networks in many populations. Simulation studies have played a crucial role in this effort, by identifying the type of network structures that have large impacts on transmission dynamics. The confluence of data, theory, and methods has created a clear agenda for quantifying the influence of networks on HIV transmission risks. While many of the pieces of the emerging research program are now in place, there is a wide gulf between the network data and the current simulation modeling frameworks. Simulations typically create network effects indirectly, by varying parameters of some convenient function to produce a change in simulated networks. The observable network measures are thus outcomes of the model, rather than inputs. While this strategy has been very useful for orienting initial research, it has hamstrung our ability to evaluate the empirical transmission risk in observed networks. We propose a solution here that is based on statistical models for random graphs: it can be used to estimate network parameters from data, and then simulate networks with those properties. Specifically, we propose to: (1) develop random graph models for estimating network parameters and simulating evolving networks, with epidemiologically relevant formal tests for goodness-of-fit. Both the estimation and simulation methods will be based on a common Markov Chain Monte Carlo (MCMC) algorithm, which will enable researchers for the first time to simulate networks that have the same statistical properties as those observed in real data. (2) Use these methods to identify the network structures that matter most for the transmission of HIV. In particular, we will examine the independent and joint effects of needle sharing and sexual transmission, and we will test whether assortative mixing and concurrency determine the bulk of the transmission potential in a network. The methods developed will provide a systematic empirical basis for individual-level prevention strategies that focus on partnership interventions. It will enable public health professionals to identify population-level prevention strategies that make a network less vulnerable to spread. Moreover, it will identify the type of network data needed to inform such efforts.