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Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
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Predictors of HIV seroconversion in Botswana.

Yifan Cui1, Sikhulile Moyo2,3,4, Molly Pretorius Holme2

  • 1Center for Data Science, Zhejiang University, Hangzhou, Zhejiang, China.

AIDS (London, England)
|November 5, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning identified key predictors of HIV acquisition in Botswana. For women, injectable contraception and frequent sex were significant; for men, relationship type and community prevalence were key factors for HIV risk.

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Area of Science:

  • Epidemiology
  • Public Health
  • Machine Learning Applications

Background:

  • A large HIV incidence cohort in Botswana was analyzed.
  • The study utilized data from a cluster-randomized HIV prevention trial across 30 communities.

Purpose of the Study:

  • To identify predictors of HIV acquisition in Botswana.
  • To leverage machine learning for identifying key HIV risk factors.

Main Methods:

  • Machine learning (randomForestSRC) was applied to analyze data from 8551 HIV-negative adults.
  • 110 potential predictors were assessed to identify significant risk factors for HIV acquisition.

Main Results:

  • Injectable hormonal contraception, sex frequency, and high community HIV prevalence predicted HIV acquisition in females.
  • Non-long-term relationships and high community HIV prevalence predicted HIV acquisition in males.
  • Specific risk factor combinations indicated significantly elevated HIV acquisition probabilities for both sexes.

Conclusions:

  • Machine learning efficiently identified key HIV risk predictors from numerous variables.
  • Identified factors can inform targeted HIV prevention strategies in Botswana.