Bridging Statistical Inference and Mechanistic Network Models for HIV/AIDS

连接艾滋病毒/艾滋病的统计推断和机制网络模型

• 批准号: 10179312
• 负责人: Jukka-Pekka Onnela
• 金额: $ 55.43万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-02 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10179312
• 关键词: AIDS prevention; AIDS/HIV problem; Adoption; Automobile Driving; Bayesian Analysis; Behavior; Behavior Therapy; Behavioral; Biological; Cluster randomized trial; Communicable Diseases; Communities; Computer Models; Computer software; Data; Development; Dimensions; Disease; Epidemic; Ethics; Evaluation; Evolution; Family; Foundations; Goals; HIV; Health Sciences; Human; Individual; Infection; Intervention; Learning; Likelihood Functions; Logistics; Machine Learning; Mathematics; Methodology; Methods; Modeling; Pattern; Physics; Population; Prevention Measures; Prevention strategy; Probability; Process; Property; Public Health; Pythons; Research; Research Personnel; SET Domain; Science; Specific qualifier value; Statistical Methods; Statistical Models; Structure; Time; Uncertainty; base; effective intervention; high dimensionality; implementation intervention; indexing; innovation; insight; interest; member; network models; open source; pandemic disease; pathogen; pre-exposure prophylaxis; simulation; statistics; stem; tool; treatment adherence; treatment strategy

Network models are used to investigate the spread of HIV/AIDS, but rather than assuming that the members of a population of interest are fully mixed, the network approach enables individual-level specification of contact patterns by considering the structure of connections among the members of the population. By representing individuals as nodes and contacts between pairs of individuals as edges, this network depiction enables identification of individuals who drive the epidemic, allows for accurate assessment of study power in cluster- randomized trials, and makes it possible to evaluate the impact of interventions on the individuals themselves, their partners, and the broader network. There are currently two major mathematical paradigms to the modeling of networks: the statistical approach and the mechanistic approach. In the statistical approach, one specifies a model that states the likelihood of observing a given network, whereas in the mechanistic approach one specifies a set of domain-specific mechanistic rules at the level of individual nodes, the actors in the network, that are used to evolve the network over time. Given that mechanistic models directly model individual-level behaviors – modification of which is the foundation of most prevention measures – they are a natural fit for infectious diseases. Another attractive feature of mechanistic models is their scalability as they can be implemented for networks consisting of thousands or even millions of nodes, making it possible to simulate population-wide implementation of interventions. Lack of statistical methods for calibrating these models to empirical data has however impeded their use in real-world settings, a limitation that stems from the fact that there are typically no closed-form likelihood functions available for these models due the exponential increase in the number of ways, as a function of network size, of arriving at a given observed network. We propose to overcome this gap by advancing inferential and model selection methods for mechanistic network models, and by developing a framework for investigating their similarities with statistical network models. We base our approach on approximate Bayesian computation (ABC), a family of methods developed specifically for settings where likelihood functions are intractable or unavailable. Our specific aims are the following. Aim 1: To develop a statistically principled framework for estimating parameter values and their uncertainty for mechanistic network models. Aim 2: To develop a statistically principled method for model choice between two competing mechanistic network models and estimating the uncertainty surrounding this choice. Aim 3: To establish a framework for mapping mechanistic network models to statistical models. We also propose to implement these methods in open source software, using a combination of Python and C/C++, to facilitate their dissemination and adoption. We believe that the research proposed here can help harness mechanistic network models – and with that leverage some of the insights developed in the network science community over the past decade and more – to help eradicate this disease.

网络模型用于调查艾滋病毒/艾滋病的传播，但不是假设网络模型的成员 感兴趣的人群完全混合，网络方法可以实现个人层面的接触规范 通过考虑人口成员之间的联系结构来形成模式。 个体作为节点，个体对之间的联系作为边缘，这种网络描述使得 识别推动流行病的个人，可以准确评估集群中的研究能力 随机试验，使评估干预措施对个人本身的影响成为可能， 他们的合作伙伴以及更广泛的网络目前有两种主要的数学范式。 网络建模：统计方法和机械方法。 指定一个模型，说明观察给定网络的可能性，而在机械方法中 一个在单个节点（节点中的参与者）级别指定一组特定于领域的机械规则 网络，用于随着时间的推移演化网络。考虑到机械模型直接建模。 个人层面的行为——改变行为是大多数预防措施的基础——它们是 机械模型的另一个吸引人的特点是它们的可扩展性。 可以针对由数千甚至数百万个节点组成的网络实现，从而使得 模拟在人群范围内实施干预措施。缺乏校准这些措施的统计方法。 然而，经验数据的模型阻碍了它们在现实世界中的使用，这一限制源于 事实上，由于指数的原因，这些模型通常没有可用的封闭形式似然函数 作为网络规模的函数，到达给定观察网络的方式数量增加。 建议通过推进机械网络的推理和模型选择方法来克服这一差距 模型，并开发一个框架来研究它们与统计网络模型的相似性。 我们的方法基于近似贝叶斯计算（ABC），这是专门开发的一系列方法 对于似然函数难以处理或不可用的情况，我们的具体目标如下： 开发一个用于估计参数值及其不确定性的理论原理框架 目标 2：开发一种专业原理的方法来在两个模型之间进行选择。 竞争机制网络模型并估计该选择的不确定性 目标 3： 我们还建议建立一个将机械网络模型映射到统计模型的框架。 使用Python和C/C++的组合在开源软件中实现这些方法，以方便他们 我们相信这里提出的研究可以帮助利用机制。 网络模型——并利用网络科学界发展的一些见解 过去十年多来，我们致力于帮助根除这种疾病。