Predicting and preventing HIV outbreaks in Sub-Saharan Africa, a region disproportionately
affected by the epidemic remains a significant challenge. This review explores the …

Background In generalized epidemic settings, strategies are needed to prioritize individuals
at higher risk of human immunodeficiency virus (HIV) acquisition for prevention services. We …

With highly effective human immunodeficiency virus (HIV) treatment and preexposure
prophylaxis (PrEP), our generation is poised to end the HIV pandemic. However, as the US …

Background Accurate human immunodeficiency virus (HIV) risk assessment can guide
optimal HIV prevention. We evaluated the performance of risk prediction models …

Objective: Machine learning models are not in routine use for predicting HIV status. Our
objective is to describe the development of a machine learning model to predict HIV viral …

Despite advances in reducing HIV-related mortality, persistently high HIV incidence rates
are undermining global efforts to end the epidemic by 2030. The UNAIDS Fast-track targets …

Background. Advancements in machine learning (ML) have improved the accuracy of
models that predict human immunodeficiency virus (HIV) incidence. These models have …

Introduction Digital data collection and the associated mobile health technologies have
allowed for the recent exploration of artificial intelligence as a tool for combatting the HIV …

Background Advancements in machine learning (ML) have improved the accuracy of
models that predict human immunodeficiency virus (HIV) incidence. These models have …

Background Machine learning models are not in routine use for predicting HIV status. Our
objective is to describe the development of a machine learning model to predict HIV viral …