Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation
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 prevent...
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South African Medical Association
2024-06-01
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| Series: | South African Medical Journal |
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| Online Access: | https://samajournals.co.za/index.php/samj/article/view/1439 |
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| author | T Dzinamarira E Mbunge I Chingombe D F Cuadros E Moyo I Chitungo G Murewanhema B Muchemwa G Rwibasira O Mugurungi G Musuka H Herrera |
| author_facet | T Dzinamarira E Mbunge I Chingombe D F Cuadros E Moyo I Chitungo G Murewanhema B Muchemwa G Rwibasira O Mugurungi G Musuka H Herrera |
| author_sort | T Dzinamarira |
| collection | DOAJ |
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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, there is 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.
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| format | Article |
| id | doaj-art-373ead5081e54af7948f2514d63e80db |
| institution | Kabale University |
| issn | 0256-9574 2078-5135 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | South African Medical Association |
| record_format | Article |
| series | South African Medical Journal |
| spelling | doaj-art-373ead5081e54af7948f2514d63e80db2025-02-10T12:25:53ZengSouth African Medical AssociationSouth African Medical Journal0256-95742078-51352024-06-011146b10.7196/SAMJ.2024.v114i16b.1439Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluationT Dzinamarira0E Mbunge1I Chingombe2D F Cuadros3E Moyo4I Chitungo5G Murewanhema6B Muchemwa7G Rwibasira8O Mugurungi9G Musuka10H Herrera11School of Health Systems and Public Health, University of Pretoria, Pretoria, South AfricaDepartment of Computer Science, University of Eswatini, Manzini, EswatiniChinhoyi University of Technology, Chinhoyi, ZimbabweDepartment of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, USADepartment of Public Health, Oshakati Medical Center, Oshakati, NamibiaCollege of Medicine and Health Sciences, University of Zimbabwe, Harare, ZimbabweCollege of Medicine and Health Sciences, University of Zimbabwe, Harare, ZimbabweDepartment of Computer Science, University of Eswatini, Manzini, EswatiniHIV, STIs, Viral Hepatitis and other Viral Diseases Control Division, Rwanda Biomedical Center, Kigali, RwandaAIDS and TB Program, Ministry of Health and Child Care, Harare, ZimbabweInternational Initiative for Impact Evaluation, Harare, Zimbabwe School of Pharmacy and Biomedical Sciences, University of Portsmouth, UK 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, there is 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. https://samajournals.co.za/index.php/samj/article/view/1439HIVMachine learningPredictionModellingAfrica |
| spellingShingle | T Dzinamarira E Mbunge I Chingombe D F Cuadros E Moyo I Chitungo G Murewanhema B Muchemwa G Rwibasira O Mugurungi G Musuka H Herrera Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation South African Medical Journal HIV Machine learning Prediction Modelling Africa |
| title | Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation |
| title_full | Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation |
| title_fullStr | Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation |
| title_full_unstemmed | Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation |
| title_short | Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation |
| title_sort | using machine learning models to plan hiv services emerging opportunities in design implementation and evaluation |
| topic | HIV Machine learning Prediction Modelling Africa |
| url | https://samajournals.co.za/index.php/samj/article/view/1439 |
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