Evidence map›Paper›PMID 39716332›Full record

ArticleBMC research notes2024

Machine learning approach and geospatial analysis to determine HIV infection, awareness status, and transmission knowledge among adults in Sub-Saharan Africa.

Abel Endawkie, Birhan Asmame Miheretu, Anteneh Yalew, Peter S Nyasulu, Getahun Worku, Ashebir Asaminew, Bayuh Asmamaw Hailu

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Article in BMC research notes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Abel EndawkieDepartment of Epidemiology and Biostatistics, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia. abelendawkie@gmail.com.
Birhan Asmame MiheretuDepartment of Geography and Environmental Studies, Wollo University, Dessie, Ethiopia.
Anteneh YalewHealth Sciences Research Office, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Peter S NyasuluDivision of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
Getahun WorkuDepartment of Statistics, College of Natural Science, Wollo University, Dessie, Ethiopia.
Ashebir AsaminewDevelopment Bank, Addis Ababa, Ethiopia.
Bayuh Asmamaw HailuDivision of Epidemiology and Biostatistics, Monitoring and Evaluation, Wollo University, Dessie, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHIV/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.

methodsThe 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.

resultsThe 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.

conclusionThe 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.

Indexed as

Health Knowledge, Attitudes, PracticeHIV InfectionsMachine LearningAdolescentAdultAfrica South of the SaharaAwarenessFemaleHumansMaleMiddle AgedPrevalenceSpatial AnalysisYoung AdultGeospatial analysisHIV infection awareness statusHIV transmission knowledgeMachine learning approachSub-Saharan Africa

Identifiers

PMID39716332
PMCPMC11667932

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