Harnessing Predictive Modeling to Advance HIV Self-Testing in SubSaharan Africa: A Viewpoint on Equity-Driven Implementation
ABSTRACT Predictive modeling presents a transformative opportunity to enhance HIV self-testing (HIVST) uptake across SubSaharan Africa (SSA). While machine learning techniques such as Random Forest (RF) and Classification and Regression Trees (CART) offer powerful tools for identifying high-risk po...
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| Format: | Article |
| Language: | English |
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Makhdoomi Printers
2025-07-01
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| Series: | Global Journal of Medicine and Public Health |
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| Online Access: | https://nicpd.ac.in/ojs-/index.php/gjmedph/article/view/4150 |
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| author | Felix Emeka Anyiam Maureen Nokuthula Sibiya Olanrewaju Oladimeji |
| author_facet | Felix Emeka Anyiam Maureen Nokuthula Sibiya Olanrewaju Oladimeji |
| author_sort | Felix Emeka Anyiam |
| collection | DOAJ |
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ABSTRACT
Predictive modeling presents a transformative opportunity to enhance HIV self-testing (HIVST) uptake across SubSaharan Africa (SSA). While machine learning techniques such as Random Forest (RF) and Classification and
Regression Trees (CART) offer powerful tools for identifying high-risk populations and optimizing HIVST
distribution, their adoption in public health remains limited. This Viewpoint examines how stigma, economic
constraints, and urban-centric data biases hinder the integration of predictive analytics into HIVST and argues for
equity-driven implementation strategies. The authors argue that leveraging predictive modeling requires an
ethical, community-driven approach that prioritizes fairness, transparency, and real-world applicability. Without
inclusive implementation strategies, predictive analytics risks reinforcing disparities rather than reducing them.This
article presents a strategic framework for integrating machine learning into HIVST policy and practice while
addressing concerns around data bias, public trust, and stakeholder engagement. By bridging the gap between
artificial intelligence (AI) and global health equity, predictive modeling can serve as a catalyst for achieving UNAIDS’
2030 goals for broad, equitable HIV testing access.
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| format | Article |
| id | doaj-art-a7e8fe99ae33442b8c9c878efc2060d1 |
| institution | Matheson Library |
| issn | 2277-9604 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Makhdoomi Printers |
| record_format | Article |
| series | Global Journal of Medicine and Public Health |
| spelling | doaj-art-a7e8fe99ae33442b8c9c878efc2060d12025-07-02T17:07:06ZengMakhdoomi PrintersGlobal Journal of Medicine and Public Health2277-96042025-07-01142Harnessing Predictive Modeling to Advance HIV Self-Testing in SubSaharan Africa: A Viewpoint on Equity-Driven ImplementationFelix Emeka AnyiamMaureen Nokuthula SibiyaOlanrewaju Oladimeji ABSTRACT Predictive modeling presents a transformative opportunity to enhance HIV self-testing (HIVST) uptake across SubSaharan Africa (SSA). While machine learning techniques such as Random Forest (RF) and Classification and Regression Trees (CART) offer powerful tools for identifying high-risk populations and optimizing HIVST distribution, their adoption in public health remains limited. This Viewpoint examines how stigma, economic constraints, and urban-centric data biases hinder the integration of predictive analytics into HIVST and argues for equity-driven implementation strategies. The authors argue that leveraging predictive modeling requires an ethical, community-driven approach that prioritizes fairness, transparency, and real-world applicability. Without inclusive implementation strategies, predictive analytics risks reinforcing disparities rather than reducing them.This article presents a strategic framework for integrating machine learning into HIVST policy and practice while addressing concerns around data bias, public trust, and stakeholder engagement. By bridging the gap between artificial intelligence (AI) and global health equity, predictive modeling can serve as a catalyst for achieving UNAIDS’ 2030 goals for broad, equitable HIV testing access. https://nicpd.ac.in/ojs-/index.php/gjmedph/article/view/4150Keywords: Predictive modeling, HIV self-testing (HIVST), Public health intervention, Sub-Saharan Africa, Machine learning, Equity in healthcare. |
| spellingShingle | Felix Emeka Anyiam Maureen Nokuthula Sibiya Olanrewaju Oladimeji Harnessing Predictive Modeling to Advance HIV Self-Testing in SubSaharan Africa: A Viewpoint on Equity-Driven Implementation Global Journal of Medicine and Public Health Keywords: Predictive modeling, HIV self-testing (HIVST), Public health intervention, Sub-Saharan Africa, Machine learning, Equity in healthcare. |
| title | Harnessing Predictive Modeling to Advance HIV Self-Testing in SubSaharan Africa: A Viewpoint on Equity-Driven Implementation |
| title_full | Harnessing Predictive Modeling to Advance HIV Self-Testing in SubSaharan Africa: A Viewpoint on Equity-Driven Implementation |
| title_fullStr | Harnessing Predictive Modeling to Advance HIV Self-Testing in SubSaharan Africa: A Viewpoint on Equity-Driven Implementation |
| title_full_unstemmed | Harnessing Predictive Modeling to Advance HIV Self-Testing in SubSaharan Africa: A Viewpoint on Equity-Driven Implementation |
| title_short | Harnessing Predictive Modeling to Advance HIV Self-Testing in SubSaharan Africa: A Viewpoint on Equity-Driven Implementation |
| title_sort | harnessing predictive modeling to advance hiv self testing in subsaharan africa a viewpoint on equity driven implementation |
| topic | Keywords: Predictive modeling, HIV self-testing (HIVST), Public health intervention, Sub-Saharan Africa, Machine learning, Equity in healthcare. |
| url | https://nicpd.ac.in/ojs-/index.php/gjmedph/article/view/4150 |
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