Leveraging In-Silico Surveillance and AI-Powered Modeling of West African Arenavirus Genomic Data for Early Lassa Fever Outbreak Prediction

Introduction: Lassa Fever continues to pose a significant public health burden in West Africa, particularly in regions such as Nigeria, Sierra Leone, Ghana, and Guinea-Bissau, where surveillance gap hinders timely epidemic response. This study proposes a digital innovation framework that integrates bioinformatics, artificial intelligent (AI), and Mobile health platforms to enhance outbreak predictions and epidemic intelligence. Methods: Utilising curated genomic datasets from the NCBI Virus Database and analysing Lassa mammarenavirus isolates across human and rodent reservoirs. Key accessions included NC_004297(Josiah, Sierra Leone), MK345515.1(AV, Nigeria), MH979661.1(NL-1072H, Nigeria), MH979663.1(NL-1087H, Ghana), and MH979662.1(NL-1079H, Guinea Bisau). Using tools such as MAFFT for multiple sequence alignment and Nextstrain’s augur and auspice pipelines, we mapped phylogenetic relationship and inferred regional viral evolution patterns. To support predictive analytics, we trained machine learning models, including Random Forest and Long Short-Term Memory (LSTM) algorithms, using genomic features and outbreak meta data. The pipeline was implemented in python, leveraging libraries such as scikit-learn, TensorFlow, and Pandas. Results: Results showed that high-risk genomic signatures and emerging variants could be link to geographic clusters ahead of confirm outbreak reports. Conclusion: Proposing a regionally integrated West African Lassa Virus Gene Bank Interface to unify in-silicon surveillance with mobile case reporting platforms, enabling frontline health actors to receive early alerts and deploy countermeasures efficiently. This AI-enabled genomic surveillance model demonstrates the viability of low-cost, scalable epidemic control in resource-limited settings and offers a transferable framework for emerging infectious diseases across Africa.