<![CDATA[Lassa fever remains a major public health concern in Nigeria because of its recurrent outbreaks, high morbidity, and fluctuating case fatality rates. This study investigates the geographical distribution and temporal dynamics of Lassa fever in Nigeria from 2020 to 2025 using spatial epidemiology and predictive analytics. Surveillance data obtained from the Nigeria Centre for Disease Control were analyzed through geospatial mapping to visualize confirmed cases and deaths at the state and Local Government Area levels, while Bayesian hierarchical spatial models, specifically the Integrated Nested Laplace Approximation and Besag-York-Mollié models, were applied to generate predictions and identify persistent and emerging hotspots. The findings show that a small cluster of states, particularly Ondo, Edo, and Bauchi, consistently accounted for more than 70% of annual confirmed cases. Case fatality rates ranged from 16% to 21% during the study period, with notable increases in 2023 and 2025. The hotspot maps further reveal marked spatial heterogeneity in disease risk, shaped by ecological suitability for rodent reservoirs, population density, and disparities in health systems. In addition, the predictive outputs show strong agreement with historical data, confirming the usefulness of the models for early warning. The study concludes that integrating spatial mapping with predictive modeling provides a robust framework for strengthening Lassa fever surveillance and response in Nigeria. These findings contribute a scalable and adaptable methodological approach that can support outbreak forecasting, resource optimization, timely intervention in high-risk areas, and broader data-driven epidemic intelligence for infectious disease control.]]>
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