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.

项目摘要 要实现到2030年消灭艾滋病的目标，就需要覆盖所有人口，特别是那些艾滋病感染率最高的人口。 注射毒品（PWID）。PWID继续经历一些最具爆炸性的 艾滋病毒在全球蔓延。注射毒品越来越多地在低感染率和高感染率国家中造成新的艾滋病毒感染。 中等收入国家（LMICs）和曾经看到艾滋病毒感染者发病率显著下降的国家。 即使在艾滋病毒感染者发病率显著下降的国家，如美国， 处方阿片类药物的使用导致海洛因注射增加，过量服用率增加， 艾滋病。在难以接触到的人群（如艾滋病感染者）中防治艾滋病毒流行病，需要有针对性地 这种方法考虑了超出个人因素的多种风险水平。看着 通过网络科学的透镜预防艾滋病毒，可以使我们能够研究和解决健康差距， 社会和结构层面。对PWID的有限网络研究表明， 网络在艾滋病毒传播中发挥着重要作用，可进一步利用网络进行有针对性的干预 接近。然而，网络数据的枚举和分析可能具有挑战性， 需要工具和分析方法来充分利用艾滋病毒社交网络的力量 预防工作。鉴于收集PWID之间社会关系数据的挑战，通过发现 网络数据的代理或方法来估算网络，我们可以利用这些连接来中断艾滋病毒 传输基于网络的干预不仅可以更有效地中断社区 传输比个人层面的方法，但也可以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，沿着详细的个人- 2016-21年，来自印度新德里2,500多个PWID的水平数据和HIV序列。旨在 探索使用机器学习和病毒遗传学作为规避网络的潜在途径 列举挑战，并制定新的分析战略，以监测流行病， 在一个城市采取有效和资源节约型的干预办法。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的影响， 可以用来告知广泛的社会，行为和药理学干预。制作网络 更容易获得的数据可以导致新的艾滋病毒预防方法， 这将有助于提高对艾滋病毒/艾滋病的认识，并有助于更好地了解这一流行病的动态。

项目成果

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。

• DOI: 10.1101/2023.01.11.523611
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Duchen,Dylan;Clipman,Steven;Vergara,Candelaria;Thio,ChloeL;Thomas,DavidL;Duggal,Priya;Wojcik,GenevieveL
• 通讯作者: Wojcik,GenevieveL

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

• DOI: 10.1093/ofid/ofac481
• 发表时间: 2022-10
• 期刊: Open forum infectious diseases
• 影响因子: 4.2

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

• DOI: 10.1126/sciadv.abf0158
• 发表时间: 2022-10-21
• 期刊: Science advances
• 影响因子: 13.6