Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation
DOI:
https://doi.org/10.7196/SAMJ.2024.v114i16b.1439Keywords:
HIV, Machine learning, Prediction, Modelling, AfricaAbstract
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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Copyright (c) 2024 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
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