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.

项目概要 在过去的二十年里，越来越多的文献表明，社会因素既推动了毒品的使用，也推动了毒品的使用。 传染病，如艾滋病毒。同时，流行病模型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力量和复杂性，但它们仍然 通常缺乏现实的社会数据，因为反映高危人群的网络和背景数据是 通常被认为在方法上太难捕捉。符合对数据采集工具的迫切需求 这使研究人员能够了解高危人群周围的社会背景，我们的 跨学科团队开发了一个免费、开源、由 NIH BD2K 资助的软件套件，名为 Network 帆布（R01DA042711）。而Network Canvas已经大幅提升了研究人员的能力 为了快速、准确地捕获复杂的网络和上下文数据，有助于消除艾滋病毒，我们 现有工具需要优化，以进一步提高其对高危人群的及时和广泛覆盖， 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大规模捕获数据的能力，以及 覆盖最重要但难以覆盖的人群。此外，我们建议用户参与和 评估活动，为软件的设计提供信息，并严格评估其价值和对软件的影响 与流行病模型和艾滋病毒相关的网络测量。通过拟议的工作 目前的项目，我们的目标是： 1）增强研究人员的数据再现性、及时性和测量能力； 2） 提高研究参与者的可用性和可及性； 3）严格评估该工具对 性和毒品网络的测量。这项工作将带来增强的免费和开源 工具以及对捕获关键数据的工具的价值和影响的更深入的科学理解 与艾滋病毒和吸毒有关。最后，正如我们在过去五年期间所做的那样，该项目将 采用强有力的用户参与计划，与客户建立合作伙伴关系并积极采用迭代 来自相关研究社区的反馈，以塑造软件特性和功能。反馈会 被广泛寻求 - 来自我们卓有成效的科学顾问委员会 (SAB)；来自我们的合作试点 与拥有 NIH 资助的药物使用、艾滋病毒和流行病模型研究的研究人员建立合作伙伴关系 投资组合；以及来自高危人群本身。这种发展方法是确保社区的关键 我们的工具的认可、加速采用和长期可持续性。