Machine learning insights into HIV outbreak predictions in Sub-Saharan Africa

Authors

  • Charles Chukwudalu Ebulue Department of Community Medicine and Primary Healthcare, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra State, Nigeria
  • Ogochukwu Virginia Ekkeh Independent Researcher, Akwa Ibom, Nigeria
  • Ogochukwu Roseline Ebulue Nigerian Institute for Trypanosomiasis and Onchocerciasis Research (NITRA), Asaba, Nigeria
  • Chukwunonso Sylvester Ekesiobi Department of Economics, Chukwuemeka Odumegwu Ojukwu University, Igbariam, Anambra State, Nigeria

DOI:

https://doi.org/10.51594/imsrj.v4i5.1121

Abstract

Predicting and preventing HIV outbreaks in Sub-Saharan Africa, a region disproportionately affected by the epidemic remains a significant challenge. This review explores the effectiveness and challenges of using machine learning (ML) for forecasting HIV spread in high-risk areas. ML models have shown promise in identifying patterns and trends in HIV data, enabling more accurate predictions and targeted interventions. ML insights into HIV outbreak predictions leverage various data sources, including demographic, epidemiological, and behavioural data. By analysing these data, ML algorithms can identify high-risk populations and geographical areas susceptible to HIV transmission. This information is crucial for public health authorities to allocate resources efficiently and implement preventive measures effectively. Despite the potential benefits, several challenges exist in using ML for HIV outbreak predictions. These include data quality issues, such as incomplete or inaccurate data, which can affect the reliability of predictions. Additionally, the complexity of HIV transmission dynamics and the need for real-time data pose challenges for ML models. To address these challenges, researchers and practitioners are exploring innovative approaches, such as integrating multiple data sources and using advanced ML techniques. Collaborations between researchers, public health officials, and technology experts are also crucial for developing robust ML models for HIV outbreak predictions. In conclusion, while ML offers valuable insights into HIV outbreak predictions in Sub-Saharan Africa, addressing challenges such as data quality and model complexity is essential for its effective use. By overcoming these challenges, ML has the potential to significantly improve HIV prevention efforts and ultimately reduce the burden of the epidemic in the region.

Keywords:   Machine Learning, AI, HIV Outbreaks: Predictions, Insights.

Published

2024-05-05

Issue

Section

Articles