Conference Abstract | Volume 8, Abstract ELIC202556 (Poster 064) | Published: 05 Aug 2025
Stephen Ohuneni1,2,&, Oladipo Ogunbode1,2, Elizabeth Adedire3, Celestine Ameh3, Shakir Balogun1,3, Ayo Adebowale4
1Nigeria Field Epidemiology and Laboratory Training program, Abuja Nigeria, 2Nigeria Centre for Disease Control and Prevention (NCDC), Abuja Nigeria, 3African Field Epidemiology Network Abuja, Nigeria, 4Faculty of Public Health, College of Medicine, University of Ibadan, Ibadan Nigeria
&Corresponding author: Stephen Ohuneni, Nigeria Field Epidemiology and Laboratory Training program, Abuja, Nigeria, Email: adepamilerin@gmail.com
Received: 19 May 2025, Accepted: 09 Jul 2025, Published: 05 Aug 2025
Domain: Infectious Disease Epidemiology
Keywords: Lassa Fever, Outbreaks, Time-series Analysis, Nigeria
©Stephen Ohuneni 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: Stephen Ohuneni et al., Spatial analysis and time-series modelling of Lassa fever cases in Nigeria: Insights from 2018-2023 Lassa fever national surveillance data. Journal of Interventional Epidemiology and Public Health. 2025;8(Conf Proc 5):00208. https://doi.org/10.37432/JIEPH-CONFPRO5-00208
Lassa fever (LF) is a viral hemorrhagic fever endemic in Nigeria characterized by high morbidity and mortality rates. Despite significant efforts at reducing the burden of LF in Nigeria, it remains a public health concern with negative socio-economic and health impacts. Modeling and predicting LF outbreaks are crucial to ensure timely-targeted interventions. We therefore analyzed data to describe the recent five-year trend of LF, identify patterns, and made predictions.
We reviewed the LF historical surveillance data from the National Surveillance Database (Surveillance Outbreak Response Management and Analysis System – SORMAS) from January 2018 to December 2023. We summarized data using frequencies and percentages. We used a Multiplicative Time-series model to determine the trend and pattern of the Lassa fever cases. We then predicted cases for 2024 and 2025 by de-seasonalizing the observed cases using a seasonal variation index (SVI) adjustment mechanism. We employed R and QGIS (v.3.32.2) for spatial analysis.
Two of the 36 states in Nigeria―Ondo and Edo―accounted for 65% of the total confirmed cases and 53% of total mortality in the country. The Lassa fever cases followed a downward trend (β=0.1777, R2=0.0018) from 2018 to 2023. Time-series analysis shows peak periods in the first quarter (January–March) accounting for 65% of the total cases and deaths yearly. The SVI was highest in quarter 1 (2.1637), and least in quarter 4 (0.3249). The projected confirmed cases for 2024 and 2025 were 453 and 392, respectively, with peaks in the first quarter.
A downward trajectory in confirmed cases of Lassa fever was observed in Nigeria. However, peak periods are expected in the first quarter of the year. Lassa fever burden was predominant in two states. Focusing on community-driven preventive interventions before the peak periods and in the hot spot areas will facilitate Lassa fever control in Nigeria.
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