Quantifying HIV Transmission Risk in Sex/Drug Networks

描述(申请人提供)：因为传染病是 在人与人之间传播，我们对疾病传播的理解 和预防植根于人口传播动力学理论。这个 艾滋病等性传播感染(STI)的流行病学--多快 他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。这一过程的早期流行病学研究和数学模型 提供了许多导致STI控制策略变化的见解 在20世纪80年代。然而，随着艾滋病毒的到来，出现了新的挑战。 像其他不治之症一样，艾滋病毒具有广泛传播的潜力。 在合适的环境下的人群中。这使得“核心群体” 20世纪80年代以来的概念对预防艾滋病毒的效果稍差。工作量很大 在过去的15年里已经完成了工作，以确定 伙伴关系网络结构对艾滋病毒传播至关重要，并收集数据 在许多人口中建立伙伴关系网络。模拟研究已经发挥了作用 通过确定符合以下条件的网络结构类型，在此工作中发挥关键作用 对传播动力学有很大影响。数据、理论、 和方法已经制定了一个明确的议程，以量化影响 关于艾滋病毒传播风险的网络。虽然新兴市场的许多碎片 研究计划现在已经到位，网络之间存在着巨大的鸿沟 数据和当前的仿真建模框架。模拟通常 间接制造网络效果，通过改变一些方便的参数 在模拟网络中产生变化的功能。可观测网络 因此，衡量标准是模型的结果，而不是投入。虽然这件事 战略对指导最初的研究非常有用，但它阻碍了 我们在观察到的网络中评估经验传播风险的能力。 我们在这里提出了一种基于随机统计模型的解决方案 图表：它可以用来根据数据估计网络参数，然后 使用这些属性模拟网络。具体来说，我们建议：(1) 开发用于估计网络参数和仿真的随机图模型 不断发展的网络，与流行病学相关的正式测试 适合度。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，将使研究人员 第一次模拟具有相同统计数据的网络 属性与在真实数据中观察到的属性相同。(2)使用这些方法识别 对艾滋病毒传播最重要的网络结构。在……里面 特别是，我们将研究共用针头的独立和联合影响 和性传播，我们将测试混血和性传播 并发性决定了网络中的大部分传输潜力。这个 开发的方法将提供系统的经验基础，以 注重伙伴关系干预的个人层面的预防战略。 它将使公共卫生专业人员能够识别人口水平 使网络更不容易传播的预防策略。此外， 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.