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Spatiotemporal and compartmental modelling of Lassa fever transmission dynamics in Nigeria

Polycarp Dauda Madaki Olalekan Taiwo Oghenetega ThankGod Oweh Sylvia Adanma Ezenwa-Ahanene Zubairu Dalhatu Zubairu Charles Obi Ganiyat Eshikhena Dupsy Akoma E. Gayawan Ruth Manzo Sabo Z. Dambazau Jide Idris O. Ogunbode Chijioke Kaduru

Aug 2026 · Journal of interventional epidemiology and public health · 0 citations · 18 references

Abstract

Introduction: Lassa fever (LF) is a zoonotic haemorrhagic fever endemic to West Africa, with 300,000-500,000 annual cases. In Nigeria, transmission dynamics are poorly understood due to limited integration of ecological, spatial, and temporal drivers. Methods: We combined spatiotemporal and compartmental modelling using confirmed LF incidence data from the Nigeria Centre for Disease Control (NCDC, 2018-2024). A deterministic SEIAR (Susceptible-Exposed-Asymptomatic-Symptomatic-Recovered) model for humans coupled with an SEI model for rodents and an environmental contamination compartment was formulated (16 parameters). The Next-Generation Matrix (NGM) yielded the basic reproduction number (R_0 ). Global Moran’s I and Local Indicators of Spatial Association (LISA) identified hotspots. Time series decomposition and ARIMA modelling forecasted 2025–2028 incidence. Scenario analyses compared rodent control versus healthcare improvement. Results: NGM estimated R_0=1.66, higher than empirical estimates (≈1.0), indicating underappreciated zoonotic transmission. Sensitivity analysis identified human-to-human transmission rate (β_hh=+1.0) as the most positive driver and recovery rate (γ_h=- 0.73) as the most negative. Rodent population reduction (70%) reduced infections by 61.7%, compared to a 16.1% reduction from equivalent healthcare improvement. This represents a 3.8-fold greater efficacy of reservoir-targeted interventions. Spatial analysis revealed significant clustering (Global Moran’s I = 0.138, p = 0.027) with persistent hotspots in Edo and Ondo states (p < 0.01). Temporal analysis confirmed seasonality (ARIMA residuals, p = 0.381) and forecasted peak incidence of ≈350 cases in 2025–2028. Conclusion: Integrated One Health strategies prioritising rodent control alongside healthcare strengthening are most effective. Our spatial-temporal-compartmental framework provides actionable evidence for targeted LF control in Nigeria.

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