---
title: >-
  Predictors of HIV seroconversion in Botswana: machine learning analysis in a
  representative, population-based HIV incidence cohort.
description: >-
  Explore this publication on real-world evidence: Predictors of HIV
  seroconversion in Botswana: machine learning analysis in a representative,
  population-based…
date: '2024-01-01'
category: Publications
tags:
  - Botswana Harvard AIDS Institute Partnership
  - HIV
  - Abstract/Manuscript
  - Care
  - 'No'
  - Health System Partner
  - Harvard T.H. Chan School of Public Health
  - Methods/Pharmacoepi
  - R&D
canonical_url: >-
  https://www.headwaterscience.com/resources/publications/acad-botswana-harvard-aids-institute-partnership/predictors-of-hiv-seroconversion-in-botswana-machine/
source: Headwater Science
license: © 2026 Headwater Science. All rights reserved.
slug: predictors-of-hiv-seroconversion-in-botswana-machine
id: 8JNbCw68XIpkY3opUnW86
contentType: article
---

## Challenge

While HIV prevalence and incidence in Botswana have been well-characterized at the population level, the individual-level predictors of HIV seroconversion using machine learning approaches had not been identified in a population-representative dataset.

## Solution

Target RWE's HIV team and Botswana Harvard AIDS Institute Partnership collaborators applied machine learning methods to a large representative population-based Botswana dataset to identify predictors of HIV seroconversion.

## Impact

Identifying machine learning-derived predictors of HIV seroconversion provides HIV prevention program designers with a more accurate risk stratification tool for targeting prevention interventions to the highest-risk individuals.

## Use Cases / Links

Machine learning HIV seroconversion predictor identification for targeted prevention intervention in Botswana, Predictive analytics evidence supporting targeted HIV prevention program design and resource allocation