ArticleScientific reports2025
Mathematical modeling and neural network based fitting of HIV/AIDS data in the workingclass population case study from Ethiopia.
Article in Scientific reports, 2025. 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 citing paper in PubMed.
- Fractional-order modeling of HIV/AIDS dynamics in the working-class population using the ABC operator.BMC infectious diseases · 2026Article
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3 authors.
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Abstract
HIV/AIDS deeply affects society, forcing people to leave their jobs and family responsibilities. This not only impacts their lives but also has a negative effect on the economy. HIV/AIDS affects development and growth by lowering productivity, income, and poverty levels. This study examines the impact of HIV/AIDS on Ethiopia's workingclass population using mathematical and neural network-based modeling, aiming to develop an accurate predictive framework. A supervised DNN with sigmoid activation is trained on WHO data (2000-2023) to predict disease incidence and transmission. Numerical simulations show that utilizing feedforward DNN methods gives precise solutions for advanced epidemiological models. The model attained a prediction accuracy above 99% and revealed a gradual rise in HIV incidence among nonproductive individuals over two decades. Finally, the trend of HIV/AIDS infection in Ethiopia from 2024 to 2050 is forecasted. The full-blown AIDS and infected class population approaches zero. Based on our estimated parameters and numerical simulations, it is evident that the application of neural network methods marks a significant advancement in the field of epidemiological modeling.
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