A thorough understanding of population dynamics is vital for reducing the spread of infectious disease and for informing disease control. This is particularly true with HIV, due to its high transmission dependence on drug and sexual network dynamics. For stigmatized populations with large disparities in the prevalence of HIV, structural determinants (e.g., poverty, access to care, stigma ? the context surrounding the population) also drive disease. Therefore, the collection of network and contextual data is a high priority for researchers, but one that unfortunately poses enormous methodological challenges. In particular, the nature of this data has introduced new complexities in data collection, processing, and storage that limits accessibility to only those who possess the strong technical expertise and resources necessary to create bespoke solutions. Responding to this clear need, and supported by a NIDA-funded cohort study of HIV transmission (U01 DA0369349; PI: Mustanski; Co-I: Birkett), our team has developed a software tool (netCanvas-R) that can quickly and accurately capture complex network and contextual data directly from participants. Utilizing an interactive touchscreen interface, user-centered design, and modern web technologies, the collection of complex data is simplified for both researchers and participants. Our team has received extensive interest from outside research groups who wish to use our tool, but we lack the capacity for these custom configuration requests. Therefore the current project builds upon our prior work to: (1) extend and harden existing capabilities and build a standalone netCanvas software suite that is a user-friendly, generalizable, and customizable tool that will accommodate multilevel, network, longitudinal, geospatial, contextual, and behavioral data without the requirement of technical expertise; (2) ensure the sustainability of the netCanvas software suite through promotional work, engagement activities, and the production of strong training materials. This project will simplify the collection and streamline the management of complex data, thereby allowing researchers to assess more nuanced associations between contextual factors and the spread of infectious disease, and utilize this data in near real-time. Our team is particularly qualified to lead this work due to our history of successful collaboration, our complementary expertise, and our plan to seek and incorporate feedback from the network, HIV, and drug use research communities ? as evidenced by our interdisciplinary Scientific Advisory Board and two sustained test site collaborations with strong NIDA-funded HIV and drug use research portfolios.
A thorough understanding of population dynamics is vital for reducing the spread of infectious disease and for informing disease control, but the collection of network and contextual data poses enormous methodological challenges in data collection, processing, and storage. Responding to this clear need, the current project builds upon our prior work to develop and sustain a standalone software suite that will simplify the process of collecting complex data, allowing researchers to assess more nuanced associations between contextual factors and the spread of infectious disease and respond to these findings in near real time.
