Using machine learning models to plan HIV services - Emerging opportunities in design, implementation and evaluation

Aug 4, 2026

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HIV/AIDS remains a major public health and economic challenge, and progress toward the UNAIDS 95-95-95 targets is constrained by gaps in engagement in care, limited early detection tools, persistent barriers to antiretroviral therapy access, weak prevention services for vulnerable groups, and insufficient innovation in resource optimisation and distribution. A rapid review (24 Oct 2022–5 Nov 2022) screened literature from major databases to identify how machine learning can support HIV service design, prediction, implementation, and evaluation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

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REVIEW

Purpose and problem context

Rapid review methods and evidence selection

Emerging opportunities for machine learning in HIV services

This open-access article is distributed under Creative Commons licence CC-BY-NC 4.0. REVIEW Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation T Dzinamarira,1 PhD; E Mbunge,2 PhD; I Chingombe,3 MSc; D F Cuadros,4 PhD; E Moyo,5 MB ChB; I Chitungo,6 MSc; G Murewanhema,6 MMed(O&G); B Muchemwa,2 MSc; G Rwibasira,7 MD; O Mugurungi,8 MD; H Herrera,9 PhD; G Musuka,10 PhD 1 School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa 2 Department of Computer Science, University of Eswatini, Manzini, Eswatini 3 Chinhoyi University of Technology, Chinhoyi, Zimbabwe 4 Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, USA 5 Department of Public Health, Oshakati Medical Center, Oshakati, Namibia 6 College of Medicine and Health Sciences, University of Zimbabwe, Harare, Zimbabwe 7 HIV, STIs, Viral Hepatitis and other Viral Diseases Control Division, Rwanda Biomedical Center, Kigali, Rwanda 8 AIDS and TB Program, Ministry of Health and Child Care, Harare, Zimbabwe 9 School of Pharmacy and Biomedical Sciences, University of Portsmouth, UK 10 International Initiative for Impact Evaluation, Harare, Zimbabwe Corresponding author: TDzinamarira ([u19395419@up.ac.za](u19395419@up.ac.za)) HIV/AIDS remains one of the world’s most significant public health and economic challenges, with approximately 36 million people currently living with the disease. Considerable progress has been made to reduce the impact of HIV/AIDS in the past years through successful multiple HIV/AIDS prevention and treatment interventions. However, barriers such as lack of engagement, limited availability of early HIV-infection detection tools, high rates of HIV/sexually transmitted infections (STIs), barriers to access antiretroviral therapy, lack of innovative resource optimisation and distribution strategies, and poor prevention services for vulnerable populations still exist and substantially affect the attainment of the UNAIDS 95-95-95 targets. A rapid review was conducted from 24 October 2022 to 5 November 2022. Literature searches were conducted in different prominent and reputable electronic database repositories including PubMed, Google Scholar, Science Direct, Scopus, Web of Science, IEEE Xplore, and Springer. The study used various search keywords to search for relevant publications. From a list of collected publications, researchers used inclusion and exclusion criteria to screen and select relevant papers for inclusion in this review. This study unpacks emerging opportunities that can be explored by applying machine learning techniques to further knowledge and understanding about HIV service design, prediction, implementation, and evaluation. Therefore, thereis a need to explore innovative and more effective analytic strategies including machine learning approaches to understand and improve HIV service design, planning, implementation, and evaluation to strengthen HIV/AIDS prevention, treatment, and awareness strategies. S Afr MedJ 2024;114(6b):e1439. [https://doi.org/10.7196/SAMJ.2024.v114i6b.1439](https://doi.org/10.7196/SAMJ.2024.v114i6b.1439) Despite the significant progress made in previous years to reduce the catastrophic impact of HIV/AIDS, they are among the most threatening infectious diseases and continue to overburden public health systems worldwide. Key public health interventions including HIV screening, increasing universal and scaling-up of antiretroviral therapy (ART), and improving pre-exposure prophylaxis (PrEP) delivery[1] have been utilised to improve health outcomes in people living with HIV (PLHIV) and reduce new infections. However, these key HIV interventions and prevention measures continue to fall short of attaining the UNAIDS 95-95-95 targets. Emerging challenges, including a lack of effective innovative HIV awareness services,[2] lack of engagement to care, few ear

FAQs

What problems motivate using machine learning for HIV services planning?

Barriers to engagement, limited early detection tools, high HIV/STI burdens, access gaps for antiretroviral therapy, weak prevention for vulnerable populations, and limited innovation in resource optimisation and distribution constrain progress toward 95-95-95 targets.

How was the evidence gathered in this rapid review?

The review ran from 24 October 2022 to 5 November 2022 and searched multiple reputable electronic databases, using inclusion and exclusion criteria to screen and select relevant papers.

Which parts of HIV service work can machine learning help improve?

Machine learning is positioned to inform HIV service design, prediction, implementation, and evaluation, supporting targeted prevention and detection and strengthening prevention, treatment, and awareness strategies.

Aug 4, 2026

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