ArticlePLoS neglected tropical diseases2026
Spatio-temporal analysis and geostatistical modelling of onchocerciasis prevalence in Nigeria to support elimination efforts.
Article in PLoS neglected tropical diseases, 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
Nigeria has made significant progress toward the elimination of onchocerciasis through mass drug administration (MDA) of ivermectin, with ten states recently declared eligible to stop treatment following WHO-recommended epidemiological and entomological assessments. However, reliable spatial prevalence estimates remain necessary to guide elimination strategies, particularly in areas with limited surveillance. We applied model-based geostatistical analysis using Monte Carlo Maximum Likelihood Estimation to assess the spatio-temporal distribution of onchocerciasis prevalence across Nigeria from 1989 to 2024. Climatic, hydrographic, socio-economic, and topographic variables were incorporated to predict prevalence in unsampled locations. Predicted prevalence declined substantially over time. During 1997-2000, 64.9% (24/37) of states had mean predicted prevalence between 10-30%, and 5.4% (2/37) exceeded 30%. By 2017-2020, 70.3% (26/37) of states were classified within the 0-2% category, increasing to 86.5% (32/37) in 2021-2024. Nevertheless, resurgence was observed in selected areas; for example, Taraba State showed an absolute increase of 44.1 percentage points between 2013-2016 and 2021-2024 (p = 0.013). High-prevalence clusters persisted along international and interstate borders, particularly in southern Nigeria. Model performance was strong (correlation between observed and predicted prevalence: 0.80-0.86; RMSE < 0.08). The estimated spatial correlation range increased from 31.93 km (95% CI: 31.92-31.94 km) in 1997-2000 to 180.20 km (95% CI: 180.20-180.20 km) in 2021-2024. Mean annual temperature, rainfall in the driest quarter, elevation, and river flow accumulation were significant predictors of prevalence. These findings underscore the need for complementary approaches such as predictive modelling to strengthen the field surveys in planning and surveillance of the disease. To sustain the progress toward onchocerciasis elimination in Nigeria, there is a need for adaptive, climate-informed strategies, intensified surveillance in high-risk areas, and enhanced coordination, particularly in cross-border and hard-to-reach communities.
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