grant
Targeted Machine Learning to evaluate and optimize HIV prevention strategies in cluster randomized trials
Organization UNIVERSITY OF CALIFORNIA BERKELEYLocation BERKELEY, UNITED STATESPosted 1 May 2026Deadline 31 Jan 2031
NIHUS FederalResearch GrantFY202621+ years oldAIDS preventionAddressAdultAdult HumanCardiovascular DiseasesCatchment AreaChronicChronic DiseaseChronic IllnessClinicCluster AnalysesCluster AnalysisCluster randomization trialCluster randomized trialCommunitiesCommunity Health AidesComplexDataData AnalysesData AnalysisDecision MakingDiabetes MellitusEffectivenessEvaluationHIV InfectionsHIV PreventionHIV individualsHIV infected individualsHIV infected personsHIV peopleHIV positive individualsHIV positive peopleHIV riskHIV viral infectionHIV virus infectionHIV-1 infectionHIV-1 preventionHIV/AIDS preventionHealthHealth systemHeterogeneityHouseholdHypertensionIndividualInfection by HIV-1Infection from HIV-1Infection of HIV-1InterventionLocationMachine LearningMeasuresMediationMethodsModalityModelingModificationNegotiatingNegotiationOutcomePLWHPWHPersonsPrevent HIVPreventative strategyPreventionPrevention strategyPreventive strategyProceduresRandomization trialRandomizedReproducibilityRiskRoleSample SizeSamplingServicesSource CodeStratificationStructureSubgroupTarget PopulationsTestingTimeVascular Hypertensive DiseaseVascular Hypertensive DisorderWorkWorking Womenadulthoodanalytical toolarmassess effectivenesscardiovascular disordercare deliverycausal diagramcausal modelchronic disordercommunity factorcommunity health workercommunity-level factordata interpretationdata structuredetermine effectivenessdiabeteseffectiveness assessmenteffectiveness evaluationevaluate effectivenessexamine effectivenesshigh blood pressurehuman immunodeficiency virus infectionhyperpiesiahyperpiesishypertensive diseasehypertensive disorderimplementation strategyimprovedimproved outcomeindividuals infected with HIVindividuals with HIVindividuals with human immunodeficiency virusinfected with HIVinfected with human immunodeficiency virusinsightmachine based learningnovelopen sourcepeerpeople infected with HIVpeople infected with human immunodeficiency viruspeople living with HIVpeople with HIVpeople with human immunodeficiency viruspreservationprevent AIDSprevent human immunodeficiency virusrandomisationrandomizationrandomized trialrandomly assignedscale upscreeningscreeningssecondary analysissexsimulationsocial rolestrategies for implementationtheoriesusabilityuser-friendlyweb sitewebsiteworking females
Description preview
PROJECT SUMMARY/ABSTRACT
Globally, there were 1.3 million new HIV infections in 2023, despite expanded access to biomedical HIV
prevention products with high efficacy. Implementation strategies are needed to expand the reach of HIV risk
screening and to facilitate the use of biomedical prevention among persons with risk. These implementation…
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