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Africa is responsible for two‐thirds of the global total of new HIV infections. South Africa, Nigeria, Mozambique, Uganda, Tanzania, Zambia, Zimbabwe, Kenya, Malawi, and Ethiopia were responsible for 80% of HIV cases in Africa in 2014 according to the Joint United Nations Programme on HIV/AIDS (UNAIDS). This study assesses antiretroviral coverage strategies implemented by these countries after the initiation of the “Fast‐Track strategy to end the AIDS epidemic by 2030.” Data reported in this review were obtained from different e‐bibliographic including PubMed, Google Scholar, and Research Gate. Key terms were “Antiretroviral therapy,” “Antiretroviral treatment,” “HIV treatment,” “HIV medication,” “HIV/AIDS therapy,” “HIV/AIDS treatment” + each of the countries listed earlier. We also extracted data on antiretroviral therapy (ART) coverage from the UNAIDS database. About 50 papers published from 2015 to 2021 met the inclusion criteria. All 10 countries have experienced an increase in ART coverage from 2015 to 2020 with an average of 47.6% increment. Nigeria recorded the highest increase in the rate of ART coverage (72% increase) while Ethiopia had the least (30%). New strategies adopted to increase ART coverage and retention in most countries were community‐based models and the use of mobile health technology rather than clinic‐based. These strategies focus on promoting task shifting, door‐to‐door access to HIV services, and a long‐term supply of antiretroviral medications. Most of these strategies are still in the piloting stage. However, some new strategies and frameworks have been adopted nationwide in countries like Mozambique, Tanzania, Zambia, Zimbabwe, Kenya, and Malawi. Identified challenges include lack of funding, inadequate testing and surveillance services, poor digital penetration, and cultural/religious beliefs. The adoption of community‐based and digital health strategies could have contributed to increased ART coverage and retention. African countries should facilitate nationwide scaling of ART coverage strategies to attain the 95–95–95 goal by 2030.

IntroductionMaternal health is a critical aspect of public health that affects the wellbeing of both mothers and infants. Despite medical advancements, maternal mortality rates remain high, particularly in developing countries. AI-based models provide new ways to analyze and interpret medical data, which can ultimately improve maternal and fetal health outcomes.MethodsThis study proposes a deep hybrid model for maternal health risk classification in pregnancy, which utilizes the strengths of artificial neural networks (ANN) and random forest (RF) algorithms. The proposed model combines the two algorithms to improve the accuracy and efficiency of risk classification in pregnant women. The dataset used in this study consists of features such as age, systolic and diastolic blood pressure, blood sugar, body temperature, and heart rate. The dataset is divided into training and testing sets, with 75% of the data used for training and 25% used for testing. The output of the ANN and RF classifier is considered, and a maximum probability voting system selects the output with the highest probability as the most correct.ResultsPerformance is evaluated using various metrics, such as accuracy, precision, recall, and F1 score. Results showed that the proposed model achieves 95% accuracy, 97% precision, 97% recall, and an F1 score of 0.97 on the testing dataset.DiscussionThe deep hybrid model proposed in this study has the potential to improve the accuracy and efficiency of maternal health risk classification in pregnancy, leading to better health outcomes for pregnant women and their babies. Future research could explore the generalizability of this model to other populations, incorporate unstructured medical data, and evaluate its feasibility for clinical use.