Spatio-temporal analysis and geostatistical modelling of onchocerciasis prevalence in Nigeria to support elimination efforts

TL;DR

This study uses advanced spatial modeling to track and predict onchocerciasis prevalence in Nigeria, showing a national decline but local resurgences near borders.

Contribution

The study introduces a spatio-temporal geostatistical model to estimate onchocerciasis prevalence and identify risk factors, supporting targeted elimination strategies.

Findings

Predicted onchocerciasis prevalence in Nigeria declined significantly from 1997–2000 to 2021–2024, with most states now in low-prevalence categories.

Localized resurgence was observed in areas like Taraba State and along international/interstate borders, particularly in southern Nigeria.

Environmental factors such as temperature, rainfall, elevation, and river proximity were key predictors of infection risk.

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.

Onchocerciasis (river blindness) remains a public health concern in Nigeria, although substantial progress has been made through long-term mass drug administration of ivermectin. Ten states have recently met World Health Organization criteria to stop treatment, but gaps in surveillance data make it difficult to fully understand where transmission may persist. We used advanced spatial modelling techniques to reconstruct changes in onchocerciasis prevalence across Nigeria from 1989 to 2024 and to predict risk in areas without survey data. Our findings show a marked national decline in prevalence over time, with most states now falling within very low prevalence categories. However, localized resurgence was observed in some areas, particularly near international and interstate borders. Environmental factors such as temperature, rainfall, elevation, and proximity to rivers were important predictors of infection risk. These results highlight the value of predictive modelling to complement field surveys and support targeted, climate-informed strategies to sustain elimination efforts.

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