HIV Risk Prediction and Prevention Strategies

Efforts to predict and prevent HIV infection increasingly combine epidemiological modelling, machine-learning analytics and tailored biomedical interventions. Epidemiological frameworks identify key behavioural, demographic and social determinants of transmission, while machine-learning algorithms extract complex patterns from large datasets to stratify individuals by future risk. Biomedical prevention, notably pre-exposure prophylaxis (PrEP) and universal antiretroviral therapy (ART), is most effective when targeted to those at highest risk. A “combination prevention” paradigm integrates behavioural counselling, community-based testing, digital tools and policy measures to reduce incidence globally. Personalised risk scores and web-based calculators empower individuals to understand their own vulnerability and seek timely testing or prophylaxis. At the population level, predictive models guide resource allocation and optimise screening strategies in high-burden settings, while ethical and equity considerations shape the design of algorithms and digital platforms to avoid exacerbating stigma or discrimination.

Research from Nature Portfolio

A large community-based screening study characterised HIV prevalence and incidence among young men in a metropolitan US setting. Analysis of over 11 000 cisgender men, including men who have sex with men (MSM) aged ≤ 24 years, revealed an incidence exceeding 3 per 100 person-years in young MSM and a positivity rate markedly higher than in older cohorts. Young MSM reporting condomless serodiscordant anal intercourse, bacterial sexually transmitted infections and substance use were identified as priority candidates for targeted prevention. The study demonstrates how routine community testing can generate granular incidence data to inform age- and behaviour-specific interventions and optimise PrEP uptake in vulnerable subgroups.

HIV Risk Prediction and Prevention Strategies publication trend

The graph below shows the total number of articles in hiv risk prediction and prevention strategies across all publications each year (not limited to Nature Index journals).

Technical terms

Pre-exposure prophylaxis (PrEP): Preventive antiretroviral treatment taken by HIV-negative individuals to reduce the risk of infection.

Antiretroviral therapy (ART): Combination drug regimen that suppresses HIV replication in infected individuals, reducing transmission risk.

Machine learning: Computational methods that enable predictive models to learn patterns from data without explicit programming.

XGBoost: An optimised gradient-boosting algorithm widely used for its high accuracy and efficiency in classification tasks.

Area under the receiver operating characteristic curve (AUC): A measure of a model’s ability to discriminate between positive and negative outcomes, with values closer to 1 indicating superior performance.

Risk score: A quantitative index combining multiple predictors to estimate an individual’s probability of HIV acquisition over a defined period.

References

1. Predicting HIV infection in the decade (2005–2015) pre-COVID-19 in Zimbabwe: A supervised classification-based machine learning approach. PLOS Digital Health (2023).
2. Ethical Considerations for Artificial Intelligence Applications for HIV. AI (2024).
3. HIV Infection Rates and Risk Behavior among Young Men undergoing community-based Testing in San Diego. Scientific Reports (2016).
4. Web-Based Risk Prediction Tool for an Individual's Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study. Journal of Medical Internet Research (2022).