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Viral Infections and Outbreaks Research · Journal article
American Journal of Bioscience and Bioinformatics · August 3, 2026
Encouraging direction, but not yet definitive.
This is a national ecological study documenting a strong seasonal pattern in Lassa fever transmission in Nigeria between 2020 and 2025, with temperature, rainfall, humidity, and seasonality identified as independent climatic predictors using Negative Binomial Regression. The findings provide quantitative evidence for climate-informed surveillance and preparedness strategies, but remain observational and Nigeria-specific without direct clinical intervention or global generalizability.
Ecological analysis with Negative Binomial Regression. All confirmed Lassa fever cases reported in Nigeria between 2020 and 2025 matched with national meteorological records.. n = 1,404. Nigeria.
77.1% of confirmed cases occurred during the dry season, with peak transmission between January and March Disease incidence during dry season was approximately 113% higher than wet season after adjusting for climatic factors Temperature was a significant positive predictor (IRR = 1.024, p < 0.001)
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Clinicians and public health professionals in Nigeria should use these findings to anticipate higher Lassa fever incidence during dry season months (January–March) and support climate-informed surveillance and early warning systems. The quantified climatic drivers may inform timing of preparedness campaigns and resource allocation, though the findings are specific to Nigeria and require validation in other endemic regions.
A well-designed ecological analysis with appropriate statistical methods quantifying climatic drivers of Lassa fever transmission in a defined geographic setting, but based on observational data without a clinical outcome or intervention, and limited to a single country over a recent 5-year window.
As stated by the source record.
Quoted from the source exactly as published.
Clinicians and public health professionals in Nigeria should use these findings to anticipate higher Lassa fever incidence during dry season months (January–March) and support climate-informed surveillance and early warning systems. The quantified climatic drivers may inform timing of preparedness campaigns and resource allocation, though the findings are specific to Nigeria and require validation in other endemic regions.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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.
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