Fig 1.
Duration of MAM rounds in different States (implementation unit) across Nigeria.
This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World Bank Data Catalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria), an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Table 1.
Data sources and properties of environmental, Socio-economic and climatic covariates explored to model the prevalence of Onchocerciasis in Nigeria.
Fig 2.
Reported prevalence of onchocerciasis at community level in Nigeria between 1989 to 2024.
This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World Bank Data Catalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria), an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 3.
Reported prevalence of onchocerciasis at state level (transmission zones) in Nigeria between 1989 to 2024.
States in grey connotes missing data. This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World Bank Data Catalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria), an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 4.
Reported prevalence of onchocerciasis at LGA level (Sub-units) in Nigeria between 1989 to 2024.
LGAs in grey connotes missing data. This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World Bank Data Catalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria), an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 5.
Change in prevalence of onchocerciasis in Nigeria over the period of 30 years.
States in grey connotes missing data. This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World Bank Data Catalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria), an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 6.
Map showing predicted spatio-temporal prevalence risk of onchocerciasis in Nigeria at specific location level.
This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World Bank Data Catalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria), an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 7.
Map showing classified predicted spatio-temporal prevalence risk of onchocerciasis in Nigeria at State (Transmission Zones) level.
This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World Bank Data Catalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria), an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Table 2.
Estimates (median; 95% confidence interval) of the final geostatistical models for spatio-temporal predictions of onchocerciasis in Nigeria.
Fig 8.
Marginal effects of co-variates on the prevalence of onchocerciasis in Nigeria, using data from all periods (1997–present).
The red curves show the posterior mean marginal effects of each covariate from generalized additive model, adjusted for all other variables. Grey shaded bands represent the 95% credible intervals. Effects are presented on the predicted prevalence scale. Increasing or decreasing trends indicate positive or negative associations, respectively, while wider intervals at distributional extremes reflect increased uncertainty due to sparse observations.