Molecular Networks and Deep Learning for Targeted HIV Interventions among PWID

项目总结 要实现到2030年消灭艾滋病的目标，就需要接触到所有人口，特别是那些 负担，如注射毒品者(PWID)。PWID继续经历一些最具爆炸性的 艾滋病毒在全球范围内流行。注射毒品的使用越来越多地占到了低收入和低收入人群中新的艾滋病毒感染 中等收入国家(LMIC)和曾经在残疾人中艾滋病毒发病率显著下降的国家。 即使在艾滋病发病率显著下降的国家，如美国，艾滋病发病率的上升 处方阿片类药物的使用导致海洛因注射增加，过量服药率增加，以及 爱滋病毒。在PWID等难以接触到的人群中防治艾滋病毒流行病需要有针对性的 考虑风险的多个级别的方法，这些风险超出了单个级别的因素。看着 通过网络科学的镜头预防艾滋病毒可以让我们研究和解决健康差距 社会和结构层面。PWID中有限的网络研究表明，社会和空间 网络在艾滋病毒传播中发挥着重要作用，并可能进一步被用于有针对性的干预 接近了。但是，枚举和分析网络数据以及其他网络可能具有挑战性 需要工具和分析方法来充分利用社会网络对艾滋病毒的力量 预防工作。鉴于收集有关残疾人之间社会关系的数据存在挑战，通过发现 网络数据的代理或归罪网络的方法我们可以利用这些连接来中断HIV 变速箱。基于网络的干预可能不仅在干扰社区方面更有效 传播比个人层面的方法更好，但也可能代表最具成本效益的方法- 考虑到项目经常面临的预算和资源限制，这一点至关重要。 这项研究利用了一组罕见的纵向社会和空间网络数据以及详细的个人- 2016-21年间，跟踪了印度新德里2500多名PWID的水平数据和艾滋病毒序列。它的目标是 探索使用机器学习和病毒系统发育学作为绕过网络的潜在途径 列举挑战，并制定新的分析战略，以监测流行病和建立MOST 有效和资源节约型的城市干预方法。在实践中，这为发展 网络模型，模拟各种基于网络的干预策略对艾滋病毒发病率和 可用于广泛的社会、行为和药物干预。打造网络 更容易获得的数据可以导致新的艾滋病毒预防方法，指导官员将有限的 资源的影响最大，可以提供对疫情动态的更多了解。

PROJECT SUMMARY Meeting targets set to end AIDS by 2030 requires reaching all populations, particularly those with the highest burden, such as people who inject drugs (PWID). PWID continue to experience some of the most explosive HIV epidemics globally. Injection drug use is increasingly accounting for new HIV infections in both low- and middle-income countries (LMICs) and countries that once saw notable declines in HIV incidence among PWID. Even in countries with notable declines in HIV incidence among PWID, such as the United States, the rise of prescription opioid use has resulted in increased heroin injection, increased overdose rates, and outbreaks of HIV. Combating the HIV epidemic among hard-to-reach populations, such as PWID, requires targeted approaches that consider multiple levels of risk that extend beyond individual-level factors alone. Looking at HIV prevention through the lens of network science can allow us to study and address health disparities on a social and structural level. Limited network studies among PWID have demonstrated that social and spatial networks play a significant role in HIV transmission and may be further leveraged for targeted intervention approaches. However, network data can be challenging to enumerate and analyze, and additional network tools and analytic approaches are needed to take full advantage of the power of social networks for HIV prevention efforts. Given the challenges of collecting data on social connections among PWID, by finding proxies for network data or ways to impute networks we can harness these connections to interrupt HIV transmission. Network-based interventions may not only be more effective at interrupting community transmission than individual-level approaches but could represent the most cost-efficient approach as well – which is crucial given the budgetary and resource constraints programs often face. This study leverages a rare set of longitudinal social and spatial network data along with detailed individual- level data and HIV sequences from over 2,500 PWID in New Delhi, India followed from 2016-21. It aims to explore the use of machine learning and viral phylogenetics as a potential avenue to circumvent network enumeration challenges and produce new analytical strategies to monitor epidemics and model the most effective and resource-efficient intervention approach in a city. In practice, this affords the development of network models that simulate the effect of various network-based intervention strategies on HIV incidence and could be used to inform a wide array of social, behavioral, and pharmacologic interventions. Making network data more accessible can lead to new HIV prevention approaches that guide officials in focusing limited resources for the greatest impact and can provide a greater understanding of the epidemic dynamics.