Conference Abstract | Volume 8, Abstract ELIC2025163 (Oral 084) | Published: 11 Aug 2025
Nasir Omar Ahmed1,2,&, Ramatu Abdu Aguye3, Yahaya Muhammed3, Fatima Bello2, Yetunde Abioye2, Fatima Saleh2, Jide Idris2
1Nigeria Field Epidemiology Training Program, FCT, Nigeria, 2Nigeria Centre for Disease Control and Prevention, FCT, Nigeria, 3African Field Epidemiology Network, FCT, Nigeria
&Corresponding author: Nasir Ahmed Omar, Nigeria Centre for Disease Control and Prevention, 801 Ebitu Ukiwe Street, Jabi, Abuja, Nigeria. Email: nasir.ahmed@ncdc.gov.ng
Received: 13 Apr 2025, Accepted: 09 Jul 2025, Published: 11 Aug 2025
Domain: Infectious Disease Epidemiology
Keywords: Lassa fever, modelling, surveillance, time series analysis, seasonality, Bauchi State
©Nasir Omar Ahmed 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: Nasir Omar Ahmed et al., Epidemiology of Lassa fever in Bauchi State, Nigeria, 2021-2024: Temporal trends and forecast. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc5):00084. https://doi.org/10.37432/jieph-confpro5-00084
Lassa fever (LF) is an acute viral haemorrhagic illness caused by the Lassa virus, maintained in nature by multimammate rats. Nigeria is endemic to LF, and cases are reported throughout the year. Bauchi State in the northeast has emerged as an epicentre for LF outbreaks. Despite year-round case reporting, limited trend and seasonality analysis exist to inform timely interventions. This study applied time series modelling to describe LF trends in Bauchi from 2021 to 2024.
We conducted a retrospective analysis using 4-year LF surveillance data extracted from the Bauchi State surveillance database (2021–2024). Using Microsoft Excel, the classical multiplicative decomposition model was used to separate the time series into trend (Tt), seasonal (SV), and irregular (It) components: Yt = Tt × SV × It. A centred moving average was used to estimate the trend, seasonal subseries averaging to derive seasonal indices, and irregularities were calculated accordingly. A one-year forecast was generated based on the trend and seasonality components.
From 2021 to 2024, 2,969 suspected LF cases were reported, of which 549 (18%) were confirmed. Confirmed cases increased annually: 51 (2021), 132 (2022), 163 (2023), and 203 (2024). Using the classical multiplicative decomposition method to forecast confirmed cases, a 4% increase is projected in 2025. Case distribution by sex was at a ratio of 1:1. The age-group 20-39 years accounted for 55% of the confirmed cases, while the 0-9 years had the lowest (9%). Three LGAs, Toro (36%), Bauchi (25%), and Kirfi (23%), accounted for over 80% of confirmed cases.
Findings showcased the need to integrate analytical modelling into Nigeria’s routine disease surveillance and response frameworks. The projected increase in LF cases for 2025 underscores the need for continuous implementation of risk communication, community engagement, environmental sanitation, and strengthened surveillance, especially in high-burden LGAs in the State.
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