Network Canvas 2.0: Enhancing network data capture for drug use and HIV research

Network Canvas 2.0：增强药物使用和艾滋病毒研究的网络数据捕获

• 批准号: 10715902
• 负责人: Michelle Birkett
• 金额: $ 68.12万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10715902
• 关键词: AIDS prevention; Acceleration; Adoption; Articulation; Automobile Driving; Big Data to Knowledge; Collaborations; Collection; Communicable Diseases; Communities; Complement; Complex; Computer software; Country; Data; Development; Disease; Disparity; Drug usage; Electronic Mail; Ensure; Epidemic; Evaluation; FAIR principles; Feedback; Funding; Future; Guidelines; HIV; Health; Health Services Accessibility; Hybrids; Impairment; Incidence; Individual; Internet; Intervention; Interview; Language; Letters; Literature; Measurement; Methodology; Modeling; Modernization; National Institute of Drug Abuse; Notification; Participant; Pathway Analysis; Persons; Pharmaceutical Preparations; Population; Population Dynamics; Populations at Risk; Positioning Attribute; Protocols documentation; Public Health; Reproducibility; Research; Research Personnel; Risk; Schedule; Scientific Advances and Accomplishments; Self Administration; Shapes; Social Environment; Social Network; Software Design; Speed; Surveys; System; Technology; Testing; Time; United States National Institutes of Health; Work; cloud based; contextual factors; data quality; design; effective intervention; experience; improved; innovation; insight; marginalized population; open source; open source tool; social; social factors; study population; substance use; success; tool; uptake; usability

PROJECT SUMMARY Over the last two decades, a growing literature has demonstrated that social factors drive both drug use and infectious diseases such as HIV. Simultaneously, epidemic modeling has become vital for reducing the spread of HIV, as it allows insight into mechanisms of spread, forecasts future incidence, and provides guidance on effective intervention strategies. However, despite all their power and complexity, these epidemic models still often lack realistic social data, as network and contextual data reflective of the most at-risk populations are often deemed too methodologically challenging to capture. In line with the urgent need for data capture tools which enable researchers to understand the social context around the most at-risk populations, our interdisciplinary team has developed a free, open-source, NIH BD2K-funded software suite called Network Canvas (R01DA042711). While Network Canvas has already substantially improved the ability of researchers to quickly and accurately capture complex network and contextual data, to be useful for HIV elimination, our existing tool requires optimization to further improve its timely and broad reach to the most at-risk populations, as well as enhancements that will modernize the tool to better meet the needs of epidemic modelers. In particular, we must transition Network Canvas to a Hybrid Cloud Model, developing a cloud-based software platform that will enhance the ability of researchers to robustly capture data remotely and at scale, as well as reach the most essential but hard-to-reach populations. Additionally, we propose user-engagement and evaluation activities to inform the software's design and rigorously evaluate its value and impact on the measurement of networks relevant to epidemic modeling and HIV. Through the work proposed within the current project, we aim to: 1) Enhance data reproducibility, timeliness, and measurement for researchers; 2) Enhance the availability and accessibility for study participants; 3) Rigorously evaluate the tool's impact on the measurement of sexual and drug networks. This work will result in both an enhanced free and open-source tool and an increased scientific understanding of the value and impact of the tool for capturing crucial data relevant to HIV and drug use. Finally, just as we have done over the last five-year period, this project will employ a strong plan for user engagement where we build partnerships with and actively employ iterative feedback from relevant research communities to shape software features and functionality. Feedback would be sought widely - from our highly accomplished Scientific Advisory Board (SAB); from our collaborative pilot partnerships with researchers who hold strong NIH-funded drug use, HIV, and epidemic modeling research portfolios; and from at-risk populations themselves. This development approach is key in ensuring community buy-in, accelerated adoption, and long-term sustainability of our tools.

项目摘要 在过去的二十年里，越来越多的文献表明，社会因素既推动了吸毒， 传染病，如艾滋病。同时，流行病建模对于减少传播变得至关重要 艾滋病毒，因为它可以深入了解传播机制，预测未来的发病率，并提供指导， 有效的干预策略。然而，尽管这些流行病模型具有强大的功能和复杂性，但它们仍然 通常缺乏现实的社会数据，因为反映最危险人群的网络和背景数据 通常被认为在方法上难以捕捉。根据对数据采集工具的迫切需求 这使研究人员能够了解最危险人群周围的社会背景， 一个跨学科的团队开发了一个免费的，开源的，NIH BD 2K资助的软件套件，称为网络 画布（R 01 DA 042711）。虽然Network Canvas已经大幅提高了研究人员的能力 为了快速准确地捕获复杂的网络和背景数据，为了对消除艾滋病毒有用，我们 现有的工具需要优化，以进一步改善其及时和广泛地接触到风险最大的人群， 以及使该工具现代化的增强功能，以更好地满足流行病建模者的需求。在 特别是，我们必须将Network Canvas过渡到混合云模型，开发基于云的软件 该平台将增强研究人员远程和大规模捕获数据的能力，以及 接触到最重要但难以接触到的人群。此外，我们建议用户参与， 评估活动为软件的设计提供信息，并严格评估其价值和对 测量与流行病建模和艾滋病毒相关的网络。通过在《公约》范围内提出的工作， 目前的项目，我们的目标是：1）提高数据的可重复性，及时性和测量的研究人员; 2） 提高研究参与者的可用性和可获得性; 3）严格评估该工具对 衡量性和毒品网络。这项工作将导致增强的免费和开源 工具，并提高对该工具在获取关键数据方面的价值和影响的科学认识 与艾滋病毒和吸毒有关。最后，正如我们在过去五年中所做的那样，该项目将 采用强有力的用户参与计划，我们与之建立合作伙伴关系，并积极采用迭代 相关研究团体的反馈，以塑造软件的特点和功能。反馈将 广泛寻求-从我们高度成就的科学顾问委员会（SAB）;从我们的合作试点 与拥有强大的NIH资助的药物使用，艾滋病毒和流行病建模研究的研究人员建立伙伴关系 投资组合;以及风险人群本身。这种发展方法是确保社区 我们的工具的认可、加速采用和长期可持续性。