Artificial intelligence has proven to be a game-changing force in health sectors throughout Africa offering prospects for significant development. In sub-Saharan Africa, using AI in healthcare, especially in areas with limited resources, holds valuable promise in transforming and improving healthcare. This article takes an excellent look at how AI is being integrated into the African health sector, as well as examining policy frameworks, challenges and future possibilities. This article begins by giving an overview of AI and highlighting the groundbreaking impact of AI technologies in combating and addressing healthcare challenges that occur within African countries. Ranges from mobile-based diagnostics to precision medicine, artificial intelligence has proven its potential and capabilities in diagnosing, treating and improving healthcare operations by providing solutions to resource constraints and accessibility challenges. However, despite these advancements, there are still obstacles such as infrastructure limitations, concerns about data privacy and gaps in healthcare professionals' training that hinder the realization of AI's potential in African healthcare. This article envisions a future where the adoption of artificial intelligence is fully incorporated with community health initiatives and enhanced access to healthcare services for the betterment of healthcare across sub-Saharan African countries. While challenges and barriers like infrastructure and unequal access to healthcare persist, there is a need for governments and stakeholders to prioritize intelligence and digital health as catalysts for improving the healthcare sector in sub-Saharan Africa.

Introduction: Infectious bursal disease (IBD), caused by the infectious bursal disease Q8 virus (IBDV), is a highly contagious disease in young chickens, leading to immunosuppression with great economic importance. IBDV, a non-enveloped virus with a bipartite dsRNA genome, infects the bursa of Fabricius, causing severe gastrointestinal disease. Effective vaccines are urgently needed due to the limitations of current oral vaccines, including gastrointestinal degradation and low immunogenicity. This study designs and evaluates a multiepitope subunit vaccine using immunoinformatics.Methods: Sequences of the IBDV structural proteins VP2 and VP3 were obtained from the National Centre for Biotechnology Information) NCBI. These are structural proteins VP2 and VP3 were subjected to the Vaxijen 2.0 webserver to predict the antigenicity, ToxiPred to predict the toxicity and further analyzed to identify immunogenic epitopes of Chicken Leukocyte Antigens (CLAs) using the NetMHCpan 4.1 webserver.Results: The final vaccine construct includes 2 HTL, 21 CTL, and 7 LBL epitopes, with gallinacin-3 precursor as an adjuvant. The construct is antigenic (0.5605), non-allergenic, and non-toxic, consisting of 494 amino acids with a molecular weight of 54.88 kDa and a positive charge (pI of 9.23). It is stable, hydrophilic, and soluble. Population coverage analysis revealed a global immune coverage of 89.83%, with the highest in Europe (99.86%) and the lowest in Central America (25.01%). Molecular docking revealed strong interactions with TLR-2_1, TLR-4, and TLR-7, with TLR-7 exhibiting the highest binding affinity (−366.15 kcal/mol). Immune simulations indicated a robust immune response, with high initial IgM levels, sustained IgG, memory cell formation, and activation of T helper (Th) cells 1 and 2, Natural Killer (NK) cells, and dendritic cells, suggesting potential long-lasting immunity against IBDV.Discussion: This study presents a promising multi-epitope subunit vaccine candidate capable of effective immunization against IBDV with broad population coverage. However, further in vivo experimental validation is required to confirm its efficacy and safety.

Background: Influenza D Virus (IDV) presents a possible threat to animal and human health, necessitating the development of effective vaccines. Although no human illness linked to IDV has been reported, the possibility of human susceptibility to infection remains uncertain. Hence, there is a need for an animal vaccine to be designed. Such a vaccine will contribute to preventing and controlling IDV outbreaks and developing effective countermeasures against this emerging pathogen. This study, therefore, aimed to design an mRNA vaccine construct against IDV using immunoinformatic methods and evaluate its potential efficacy. Methods: A comprehensive methodology involving epitope prediction, vaccine construction, and structural analysis was employed. Viral sequences from six continents were collected and analyzed. A total of 88 Hemagglutinin Esterase Fusion (HEF) sequences from IDV isolates were obtained, of which 76 were identified as antigenic. Different bioinformatics tools were used to identify preferred CTL, HTL, and B-cell epitopes. The epitopes underwent thorough analysis, and those that can induce a lasting immunological response were selected for the construction. Results: The vaccine prototype comprised nine epitopes, an adjuvant, MHC I-targeting domain (MITD), Kozaq, 3′ UTR, 5′ UTR, and specific linkers. The mRNA vaccine construct exhibited antigenicity, non-toxicity, and non-allergenicity, with favourable physicochemical properties. The secondary and tertiary structure analyses revealed a stable and accurate vaccine construct. Molecular docking simulations also demonstrated strong binding affinity with toll-like receptors. Conclusions: The study provides a promising framework for developing an effective mRNA vaccine against IDV, highlighting its potential for mitigating the global impact of this viral infection. Further experimental studies are needed to confirm the vaccine’s efficacy and safety.