Seasonal and Climatic Drivers of Lassa Fever Transmission in Nigeria: A Multi-Year Ecological Analysis (2020–2025)

Abstract

Lassa fever remains a major public health challenge in Nigeria, where recurrent outbreaks continue to impose substantial health and socioeconomic burdens. Although climatic variability has been implicated in disease transmission, the relative contributions of key environmental factors to outbreak occurrence remain insufficiently quantified. This study examined the influence of temperature, rainfall, humidity, and seasonality on Lassa fever epidemiology in Nigeria using national surveillance and meteorological data collected between 2020 and 2025. A total of 1,404 observations comprising confirmed cases and associated climatic variables were analysed using descriptive statistics, Pearson correlation analysis, and Negative Binomial Regression to account for overdispersion in disease counts. The findings revealed a pronounced seasonal pattern, with 77.1% of confirmed cases occurring during the dry season and peak transmission observed between January and March. Temperature was positively associated with disease incidence, whereas rainfall exhibited a significant inverse relationship. Negative Binomial Regression identified temperature (IRR = 1.024, p < .001), rainfall (IRR = 0.987, p < .001), humidity (IRR = 1.007, p = .001), and seasonality (IRR = 2.131, p < .001) as significant predictors of Lassa fever occurrence. After adjusting for climatic factors, disease incidence during the dry season was approximately 113% higher than during the wet season. These patterns suggest that climatic conditions influence transmission through their effects on environmental suitability, rodent ecology, and opportunities for human exposure. This study provides updated national-level evidence on the climatic determinants of Lassa fever transmission in Nigeria and demonstrates the dominant role of seasonality in shaping outbreak dynamics. By simultaneously quantifying the independent effects of multiple environmental drivers using a modelling framework appropriate for overdispersed disease count data, the study advances understanding of climate-sensitive Lassa fever epidemiology and provides an evidence base for climate-informed surveillance, early warning systems, and outbreak preparedness strategies.