Conference Abstract | Volume 8, Abstract ELIC2025312 (Oral 078) | Published: 19 Aug 2025
Mohammed Abdullahi Abdulkarim1, Audu Onyemocho2, Antoine Chaillon3, Joshua Ofoli4, Ann Fortin4, Yetunde Abioye5
1AWorld Health Organization, Makurdi, Nigeria, 2Federal University of Health Sciences, Otukkpo, Nigeria, 3World Health Organization, Geneva, Switzerland, 4World Health Organization, Abuja, Nigeria, 5Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
&Corresponding author: Mohammed Abdulkarim, World Health Organization, Makurdi, Nigeria, Email: abdulkarimm@who.int
Received: 31 May 2025, Accepted: 09 Jul 2025, Published: 19 Aug 2025
Domain: Infectious Disease Epidemiology
Keywords: Lassa fever, spatial epidemiology, susceptibility modelling, Nigeria
©Mohammed Abdulkarim 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: Mohammed Abdulkarim et al., Geospatial mapping and susceptibility modelling of Lassa fever outbreaks in resource-limited settings: A case study of Benue State, Nigeria. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc5):00078. https://doi.org/10.37432/JIEPH-CONFPRO5-00078
Lassa fever remains a public health threat in Nigeria, with increasing incidence in regions previously non-endemic. Despite ongoing surveillance efforts, geospatial heterogeneity in outbreak patterns complicates timely interventions. This study examined multi-dimensional risk factors influencing Lassa fever outbreaks, analyzed their geospatial dynamics, and developed a susceptibility model for targeted intervention.
A cross-sectional, mixed-methods geospatial epidemiological study was conducted across 277 wards in Benue State. Twenty-two indicators spanning environmental, socio-cultural, epidemiological, and health system domains were normalized and integrated using a Multi-Criteria Evaluation (MCE) framework. A linear weighted sum model was applied to compute a Susceptibility Index for each ward. Spatial clustering was analyzed using GIS tools. Model validity was assessed through correlation with historical Lassa fever case and death data, and Receiver Operating Characteristic (ROC) analysis was used to define optimal risk thresholds.
The Susceptibility Index ranged from 0.14 to 0.65. ROC analysis identified 0.38 as the optimal threshold for distinguishing high-risk wards. Based on this, 51.3% of wards were classified as high-risk, 38.6% as moderate-risk (0.30–0.38), and 10.1% as low-risk (≤0.30). Spatial clustering revealed that LGAs such as Kwande, Oju, OBI, Buruku, and Gwer West had the highest concentrations of high-risk wards. Spearman correlation coefficients between the Susceptibility Index and historical cases and deaths were 0.13 and 0.09, respectively.
This study demonstrates the effectiveness of geospatial MCE modeling in identifying ward-level susceptibility to Lassa fever. The use of data-driven thresholds enhances model validity and supports targeted surveillance, resource allocation, and intervention planning. The approach offers a scalable framework for epidemic preparedness in other endemic regions.
Menu