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<p>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.</p>

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Mathematical modeling and neural network based fitting of HIV/AIDS data in the workingclass population case study from Ethiopia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月9日

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  • Abdulsamad Engida Sado,
  • Gemechis File Duressa,
  • Chernet Tuge Deressa

摘要

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.