Machine Learning–Enabled Genomic Meta-Analysis for Schistosomiasis Surveillance Across Nigeria

Abstract Schistosomiasis is a significant neglected tropical disease in Africa, especially in Nigeria, and genomic surveillance presents more opportunities to enhance disease monitoring and plan intervention. Nonetheless, the successful reuse of a metadata share presented publicly is weak due to diverse metadata quality, lacking geospatial information, and disparate annotation customary practices. The paper introduces a genomic meta-analytic machine learning-based surveillance framework of schistosomiasis utilizing free sequencing metadata sequences of Nigerian samples. Supervised learning was implemented to determine the sample classification with respect to biological and technical metadata, whereas unsupervised learning investigated the latent sample structure and biological similarity. To determine the metadata integrity and consistency across sequencing records, anomaly detection techniques were used. Aggregation of location metadata was used to perform geographic stratification to enable AI-based prioritization of the targeted intervention planning. These findings indicate that the machine learning approaches can successfully describe the schistosomiasis genomics datasets, detect leading sampling patterns, assist with high-level geographic risk stratifications. The article shows the potential of AI-based genomic monitoring of schistosomiasis as well as the existing constraints in this field and the significance of standardization of metadata and increased geographic coverage in subsequent genomics efforts.