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.
|Effective start/end date||4/1/23 → 1/31/28|
- National Institute on Drug Abuse (1R01DA057973-01A1)
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