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
基本信息
- 批准号:10469166
- 负责人:
- 金额:$ 245.63万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AIDS preventionAccountingAcquired Immunodeficiency SyndromeAddressCitiesCountryDataData CollectionDevelopmentDisease OutbreaksEpidemicFaceFutureHIVHIV InfectionsHeroinIncidenceIndiaIndividualInjecting drug userInjectionsInterruptionInterventionLeadMachine LearningModelingMolecularMonitorNetwork-basedOverdosePhylogenetic AnalysisPlayPopulationPrevention approachProxyPublic HealthResourcesRiskRoleScienceSocial NetworkUnited StatesViralbehavioral pharmacologycommunity transmissioncost efficientdeep learningexperiencehealth disparityinjection drug useinnovationlenslow and middle-income countriesmeetingsnetwork modelsopioid useprescription opioidprogramssocialsocial structuresubstance usetooltransmission process
项目摘要
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
达到到2030年结束艾滋病的目标目标需要达到所有人口,尤其是那些人口
负担,例如注射毒品的人(PWID)。 PWID继续体验一些最爆炸性的
全球艾滋病毒流行病。注射药物的使用越来越多地考虑了低和低的HIV感染
中等收入国家(LMIC)和曾经在PWID中发生艾滋病毒事件下降的国家。
即使在PWID之间的艾滋病毒事件下降的国家,例如美国,
处方阿片类药物的使用导致海洛因注射量增加,用药率升高以及爆发
艾滋病病毒。在诸如PWID之类的难以到达的人群中打击HIV流行
考虑多种级别的风险的方法,这些风险仅超出了个人级别的因素。看着
通过网络科学视角预防艾滋病毒可以使我们能够研究和解决关于健康的健康差异
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起着重要作用,可以进一步利用目标干预
方法。但是,可以挑战网络数据列举和分析,以及其他网络
需要使用工具和分析方法来充分利用社交网络的艾滋病毒的力量
预防努力。鉴于通过查找有关PWID之间社交联系的数据的挑战
网络数据的代理或估算网络的方式,我们可以利用这些连接到中断艾滋病毒
传播。基于网络的干预措施不仅可以更有效地中断社区
传输比个人级别的方法,但也可以代表最具成本效益的方法 -
考虑到预算和资源约束计划经常面临的鉴于预算和资源约束计划至关重要。
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 -
从2016 - 21年开始,印度新德里的2500多名PWID的水平数据和艾滋病毒序列。它的目的是
探索机器学习和病毒系统发育学作为绕过网络的潜在途径
枚举挑战并产生新的分析策略,以监视发作和建模最大
城市中有效且资源有效的干预方法。实际上,这提供了
网络模型模拟各种基于网络的干预策略对HIV事件的影响和
可以用来告知各种各样的社会,行为和药品干预措施。建立网络
数据更容易访问可以导致新的HIV预防方法,以指导官员进行聚焦有限
产生最大影响的资源,并可以对流行动态有更深入的了解。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:0
- 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:
- 影响因子:4.2
- 作者:
- 通讯作者:
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:
- 影响因子:13.6
- 作者:
- 通讯作者:
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Steven J. Clipman其他文献
Steven J. Clipman的其他文献
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{{ truncateString('Steven J. Clipman', 18)}}的其他基金
Leveraging the plasma virome as a biological indicator of HIV risk and transmission networks among people who inject drugs
利用血浆病毒组作为注射吸毒者中艾滋病毒风险和传播网络的生物指标
- 批准号:
10700415 - 财政年份:2023
- 资助金额:
$ 245.63万 - 项目类别:
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