ArticleOsong public health and research perspectives2026
Temporal dynamics, climate associations, and surveillance performance of Lassa fever in Nigeria, 2020-2025: a national longitudinal analysis.
Article in Osong public health and research perspectives, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundWe examined national temporal trends, laboratory-confirmation performance, and climate associations in Lassa fever surveillance in Nigeria from 2020 to 2025, and assessed the impact of the coronavirus disease 2019 (COVID-19) pandemic on surveillance metrics.
methodsWe analyzed 307 consecutive weeks of national Lassa fever surveillance data using an ecological study design. Primary outcomes included confirmed-case counts, case fatality rate (CFR), and laboratory-confirmation rate (LCR). Temporal trends were evaluated using regression models, climate associations using correlation and negative binomial regression analyses, and the impact of COVID-19 using interrupted time-series analysis.
resultsOverall, 47,029 suspected cases, 6,288 confirmed cases, and 975 deaths were reported. The mean LCR was 11.2% (standard deviation, 7.2%), declining from 18.4% in 2020 to 9.9% in 2025 (p <0.001). The overall CFR was 15.5%, with no significant downward trend over time. Confirmed cases were inversely associated with weekly rainfall (rs=-0.589, p <0.0001) and positively associated with mean temperature (rs=0.462, p<0.0001). Peak season incidence was 5.3-fold higher than that during off-peak periods. The COVID-19 lockdown period was associated with an immediate reduction in confirmed cases without a corresponding reduction in suspected cases.
conclusionLassa fever surveillance in Nigeria from 2020 to 2025 was characterized by a declining LCR, persistently elevated CFR, and a climate-linked seasonal pattern of increasing intensity, findings that are consistent with, but do not definitively establish, systemic underascertainment. CFR trends may partly reflect case-mix variation and detection bias. Because of the ecological study design, causal inferences cannot be drawn. These findings underscore the need for resilient, climate-informed, One Health surveillance systems.
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