Background: HIV/AIDS remains a major public health challenge, in Sub-Saharan Africa (SSA). In 2020, 16% of people living with HIV did not know their HIV status in SSA. Understanding the geospatial distribution of HIV infection, awareness status, and transmission knowledge is crucial for designing effective prevention and control strategies to end the HIV/AIDS pandemic by 2030. However, to the best of our literature searching the evidence of geospatial analysis and a machine learning algorithm, specifically a decision tree to decide on a Sustainability Development Goal (SDG), and to establish a clear pathway of HIV awareness status and HIV infection rates in each region of SSA is limited. Therefore, this study aims to determine HIV Infection, awareness status, and transmission knowledge among Adults in SSA using a machine learning approach and geospatial analysis.
Methods: The study used demographic and health survey data from 2009 to 2019. Machine learning algorithms and geospatial analysis techniques were employed to determine HIV infection, awareness of HIV status, and HIV transmission knowledge.
Results: The overall prevalence of HIV infection among adults in SSA from 2009 to 2019 is 4.96%. The machine learning algorithm (decision tree) indicates that infected individuals are unaware of their HIV infection, about half of them do not have HIV transmission knowledge, and more of them were found in Southern SSA. The spatial hotspots show that high HIV prevalence, low levels of HIV status awareness, and adequate transmission knowledge are specifically located in the Southern and some Eastern SSA.
Conclusion: The machine learning algorithm (decision tree) revealed that the risk of HIV infection is high among individuals who are unaware of their HIV status and lack knowledge about HIV transmission in Southern and eastern parts of Sub-Saharan Africa. The spatial analysis revealed the high-risk areas of HIV infection with low HIV status awareness and HIV transmission knowledge were located in Southern and some Eastern SSA countries. Therefore public health strategies should focus on educating individuals about the importance of knowing their HIV status, transmission knowledge, and ensuring accessible testing options in these affected regions to address the observed spatial disparities in HIV infection, HIV status awareness, and HIV transmission knowledge to achieve the 2030 Sustainable Development Goal of ending the HIV/AIDS epidemic in Africa.
Keywords: Geospatial analysis; HIV infection awareness status; HIV transmission knowledge; Machine learning approach; Sub-Saharan Africa.
© 2024. The Author(s).