Molecular Networks and Deep Learning for Targeted HIV Interventions among PWID

分子网络和深度学习对吸毒者进行针对性的艾滋病毒干预

• 批准号: 10469166
• 负责人: Steven J. Clipman
• 金额: $ 245.63万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10469166
• 关键词: AIDS prevention; Accounting; Acquired Immunodeficiency Syndrome; Address; Cities; Country; Data; Data Collection; Development; Disease Outbreaks; Epidemic; Face; Future; HIV; HIV Infections; Heroin; Incidence; India; Individual; Injecting drug user; Injections; Interruption; Intervention; Lead; Machine Learning; Modeling; Molecular; Monitor; Network-based; Overdose; Phylogenetic Analysis; Play; Population; Prevention approach; Proxy; Public Health; Resources; Risk; Role; Science; Social Network; United States; Viral; behavioral pharmacology; community transmission; cost efficient; deep learning; experience; health disparity; injection drug use; innovation; lens; low and middle-income countries; meetings; network models; opioid use; prescription opioid; programs; social; social structure; substance use; tool; transmission process

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.

项目概要 要实现到 2030 年消灭艾滋病的目标，需要覆盖所有人群，特别是那些感染率最高的人群。 负担，例如注射吸毒者（PWID）继续经历一些最具爆炸性的负担。 全球范围内的艾滋病毒流行越来越多地导致低度和低度艾滋病毒感染。 中等收入国家 (LMIC) 和曾经注射吸毒者艾滋病毒感染率显着下降的国家。 即使在美国等吸毒者艾滋病毒发病率显着下降的国家，吸毒者艾滋病毒感染率的上升 处方阿片类药物的使用导致海洛因注射量增加、吸毒过量率增加以及爆发 防治艾滋病毒在吸毒者等难以接触到的人群中的流行需要有针对性的措施。 考虑超越个人层面因素的多个风险级别的方法。 通过网络科学的视角预防艾滋病毒可以让我们研究和解决健康差异问题 社会和结构层面。有限的吸毒者网络研究表明，社会和空间层面。 网络在艾滋病毒传播中发挥着重要作用，可进一步用于有针对性的干预 然而，网络数据的枚举和分析以及附加网络可能具有挑战性。 需要工具和分析方法来充分利用社交网络的力量来治疗艾滋病毒 鉴于收集吸毒者之间社会关系数据的挑战，通过发现 网络数据代理或网络估算方法我们可以利用这些连接来中断艾滋病毒 基于网络的干预措施可能不仅可以更有效地阻断社区传播。 传播比个人层面的方法更有效，但也可以代表最具成本效益的方法 - 鉴于方案经常面临预算和资源限制，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数据 其目标是从 2016-21 年起跟踪印度新德里 2,500 多名吸毒者的水平数据和 HIV 序列。 探索使用机器学习和病毒系统发育学作为规避网络的潜在途径 枚举挑战并制定新的分析策略来监测流行病并建模最 在实践中，这为城市的发展提供了有效且资源高效的干预方法。 模拟各种基于网络的干预策略对艾滋病毒发病率和影响的网络模型 可用于为广泛的社会、行为和药物干预提供信息。 更容易获得的数据可以带来新的艾滋病毒预防方法，指导官员集中精力有限的工作 影响最大的资源，可以更好地了解流行病动态。

项目成果

Antiretroviral Drug Resistance in HIV Sequences From People Who Inject Drugs and Men Who Have Sex With Men Across 21 Cities in India.

印度 21 个城市的注射毒品者和男男性行为者的 HIV 序列中的抗逆转录病毒耐药性。

• 发表时间: 2022-10
• 影响因子: 4.2
• 作者: Clipman, Steven J;Solomon, Sunil S;Srikrishnan, Aylur K;McFall, Allison M;Gomathi, Selvamurthi;Saravanan, Shanmugam;Anand, Santhanam;Vasudevan, Canjeevaram K;Kumar, Muniratnam S;Celentano, David D;Mehta, Shruti H;Lucas, Gregory M
• 通讯作者: Lucas, Gregory M

A hepatitis B virus (HBV) sequence variation graph improves sequence alignment and sample-specific consensus sequence construction for genetic analysis of HBV.

乙型肝炎病毒 (HBV) 序列变异图可改善 HBV 遗传分析的序列比对和样本特异性共有序列构建。

• 发表时间: 2023-01-12
• 作者: Duchen, Dylan;Clipman, Steven;Vergara, Candelaria;Thio, Chloe L;Thomas, David L;Duggal, Priya;Wojcik, Genevieve L
• 通讯作者: Wojcik, Genevieve L

Deep learning and social network analysis elucidate drivers of HIV transmission in a high-incidence cohort of people who inject drugs.

深度学习和社交网络分析阐明了注射吸毒高发群体中艾滋病毒传播的驱动因素。

• 发表时间: 2022-10-21
• 影响因子: 13.6
• 作者: Clipman, Steven J;Mehta, Shruti H;Mohapatra, Shobha;Srikrishnan, Aylur K;Zook, Katie J C;Duggal, Priya;Saravanan, Shanmugam;Nandagopal, Paneerselvam;Kumar, Muniratnam Suresh;Lucas, Gregory M;Latkin, Carl A;Solomon, Sunil S
• 通讯作者: Solomon, Sunil S