Conference Abstract | Volume 8, Abstract ELIC202537 (Oral 086) | Published: 12 Aug 2025
Polycarp Dauda Madaki1,2, Zubairu Dalhatu Zubairu1,3,&, Oghenetega ThankGod Oweh1,4
1Corona Management Systems, Abuja, Nigeria, 2Department of Veterinary Tropical Diseases, University of Pretoria, Pretoria 0110, South Africa, 3Department of Public Health, Distance Learning Centre, Ahmadu Bello University, Zaria, Nigeria, 4Department of Medical Biochemistry, College of Medicine, Kaduna State University, Kaduna, Nigeria
&Corresponding author: Zubairu Dalhatu Zubairu, Department of Public Health, Distance Learning Centre, Ahmadu Bello University, Zaria, Nigeria, Email: zzubairu@mmf.coronams.com, zdzubairu@dlc.abu.edu.ng
Received: 03 Apr 2025, Accepted: 09 Jul 2025, Published: 12 Aug 2025
Domain: Infectious Disease Epidemiology
Keywords: Lassa fever, compartmental modelling, spatiotemporal modelling, One Health
©Polycarp Dauda Madaki et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Polycarp Dauda Madaki et al. Integrating spatiotemporal and compartmental modelling to understand Lassa fever transmission in Nigeria. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc5):00086. https://doi.org/10.37432/jieph-confpro5-00086
Lassa fever which is endemic as a zoonotic haemorrhagic fever in West Africa presents escalating public health risks to Nigeria due to its expanding geographical presence as well as inadequate reporting practices. Despite its significant burden (300,000–500,000 annual cases), gaps persist in understanding transmission dynamics and optimal interventions.
We integrated spatiotemporal and compartmental modelling using 2018–2024 NCDC prevalence data. A deterministic SEIR-SEI model with 16 parameters (7 literature-derived, 4 estimated, 5 fitted using nonlinear least squares) was used to assess transmission dynamics of the disease. Spatial analyses included Moran’s I clustering and hotspot detection (Local Moran’s I), while temporal patterns were evaluated through ARIMA modelling and classical decomposition. Scenario analyses compared intervention efficacies.
The Next-Generation Matrix showed higher transmission potential (basic reproduction number = 1.66) than empirical estimates (≈1), highlighting the role of zoonotic/environmental factors. Sensitivity analysis revealed human transmission rate (1.0) and recovery rate (-0.73) as dominant drivers. Rodent control reduced infections three times more than healthcare improvement (61.67% vs. 16.07% reduction at 25% implementation). Spatial analysis confirmed strong clustering (Global Moran’s I=0.138, p=0.027) with persistent hotspots in Edo and Delta states (p< 0.01), while spatial autocorrelation declined from 2018 (I=0.144, p=0.014) to 2024 (I=0.097, p=0.086). Temporal analysis confirmed seasonal peaks (ARIMA model fit: p=0.381) and forecasted 2025–2026 outbreaks with a peak of approximately 350 cases
The study revealed that controlling Lassa fever requires integrated One Health strategies that should emphasize rodent management alongside healthcare development. Research data on spatial and temporal patterns and intervention efficiency gives public health professionals actionable insights to improve their public health response strategies.
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