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UNIVERSITY OF CALIFORNIA BERKELEY

Targeted Machine Learning to evaluate and optimize HIV prevention strategies in cluster randomized trials

Targeted Machine Learning to evaluate and optimize HIV prevention strategies in cluster randomized trials is a funding opportunity from UNIVERSITY OF CALIFORNIA BERKELEY, up to 759816, closing January 31, 2031.

Funder
UNIVERSITY OF CALIFORNIA BERKELEY
Funding
759816
Deadline
Jan 31, 2031
(1645d left)

Overview

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…

Targeted Machine Learning to evaluate and optimize HIV prevention strategies in cluster randomized trials | Go Fund It Now