Network Canvas 2.0: Enhancing network data capture for drug use and HIV research
Project Number5R01DA057973-02
Former Number1R01DA057973-01
Contact PI/Project LeaderBIRKETT, MICHELLE Other PIs
Awardee OrganizationNORTHWESTERN UNIVERSITY AT CHICAGO
Description
Abstract Text
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.
Public Health Relevance Statement
PROJECT NARRATIVE
Epidemic modeling is vital for reducing the spread of HIV and other infectious diseases, but often these
models lack realistic social data, particularly data reflective of the populations impacted by drug use and
HIV. In line with the urgent need for better data on the social systems around individuals, our team
developed a free, open-source, NIDA/NIH BD2K-funded software suite called Network Canvas. While
Network Canvas has already substantially improved the ability of researchers to quickly and accurately
capture complex network and contextual data, it requires modernization to further its timely and broad reach
and evaluation to understand its impact.
NIH Spending Category
No NIH Spending Category available.
Project Terms
AIDS preventionAccelerationAdoptionArticulationAutomobile DrivingBig Data to KnowledgeCollaborationsCollectionCommunicable DiseasesCommunitiesComplementComplexComputer softwareCountryDataDevelopmentDiseaseDisparityDrug usageElectronic MailEnsureEpidemicEvaluationFAIR principlesFeedbackFundingFutureGuidelinesHIVHealthHealth Services AccessibilityHybridsImpairmentIncidenceIndividualInternetInterventionInterviewLanguageLettersLiteratureMeasurementMethodologyModelingModernizationNational Institute of Drug AbuseNotificationParticipantPathway AnalysisPersonsPharmaceutical PreparationsPopulationPopulation DynamicsPopulations at RiskPositioning AttributeProtocols documentationPublic HealthReproducibilityResearchResearch PersonnelRiskScheduleScientific Advances and AccomplishmentsSelf AdministrationShapesSocial EnvironmentSocial NetworkSoftware DesignSpeedSurveysSystemTechnologyTestingTimeUnited States National Institutes of HealthWorkcloud basedcontextual factorsdata qualitydesigneffective interventionexperienceimprovedinnovationinsightmarginalized populationopen sourceopen source toolsocialsocial factorsstudy populationsubstance usesuccesstooluptakeusability
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