Environmental Drivers and Spatial Patterns of Lassa Fever Cases in Nigeria: A GIS-Based Approach to Dynamic Susceptibility Mapping
TL;DR
The findings indicate a pronounced seasonal pattern, with increased Lassa fever activity occurring predominantly during dry periods characterized by minimal rainfall, fewer precipitation hours, and reduced humidity, particularly between December and March.
Abstract
Lassa fever is a major yet persistently neglected viral hemorrhagic disease in West Africa, with an estimated 100,000-300,000 infections and approximately 5,000 deaths reported annually across the region. Case-fatality rates are typically above 15% among hospitalized patients and may approach 50% during epidemic periods, with Nigeria accounting for the largest reported burden of confirmed cases. This study presents a pilot multi-layer GIS framework for the spatial recording, visualization, and analysis of hemorrhagic fever infections in Nigeria, focusing primarily on Lassa fever. The framework integrates epidemiological observations with environmental information to move beyond conventional static disease maps toward dynamic assessment of disease susceptibility. Confirmed Lassa fever cases recorded between 2023 and 2025 were combined with weather variables obtained from Open-Meteo, CHIRPS precipitation data, land-use information, lithological characteristics, and elevation datasets. The datasets were temporally aggregated and statistically analyzed, while multiple GIS layers were used to identify and visualize recurring spatial patterns. Ondo, Edo, Bauchi, and Taraba consistently emerged as prominent hotspot states during the study period. Weekly temporal aggregation was adopted to correspond with the reporting frequency of official epidemic surveillance, enabling short-term environmental conditions to be examined in relation to confirmed cases. The findings indicate a pronounced seasonal pattern, with increased Lassa fever activity occurring predominantly during dry periods characterized by minimal rainfall, fewer precipitation hours, and reduced humidity, particularly between December and March. Of the 28 environmental variables examined, precipitation- and humidity-related indicators demonstrated the strongest inverse associations with weekly confirmed cases, with Spearman correlation coefficients reaching {rho} =-0.75. Air temperature showed the strongest and most consistent positive association, reaching {rho} = 0.633. These findings demonstrate the potential of integrating epidemiological surveillance and environmental GIS layers to characterize spatial and temporal patterns of Lassa fever and provide a foundation for more dynamic susceptibility mapping and environmental risk assessment in Nigeria.