Geospatial modeling of pre-intervention nodule prevalence of Onchocerca volvulus in Ethiopia as an aid to onchocerciasis elimination

Abstract

Background: Onchocerciasis is a neglected tropical filarial disease transmitted by the bites of blackflies, causing blindness and severe skin lesions. The change in focus for onchocerciasis management from control to elimination requires thorough mapping of pre-control endemicity to identify areas requiring interventions and to monitor progress. Onchocerca volvulus nodule prevalence in sub-Saharan Africa is spatially continuous and heterogeneous, and highly endemic areas may contribute to transmission in areas of low endemicity or vice-versa. Ethiopia is one such onchocerciasis-endemic country with heterogeneous O. volvulus nodule prevalence, and many districts are still unmapped despite their potential for onchocerciasis transmission.

Methodology/principle findings: A Bayesian geostatistical model was fitted for retrospective pre-intervention nodule prevalence data collected from 916 unique sites and 35,077 people across Ethiopia. We used multiple environmental, socio-demographic, and climate variables to estimate the pre-intervention prevalence of O. volvulus nodules across Ethiopia and to explore their relationship with prevalence. Prevalence was high in southern and northwestern Ethiopia and low in Ethiopia's central and eastern parts. Distance to the nearest river (RR: 0.9850, 95% BCI: 0.9751-0.995), precipitation seasonality (RR: 0.9837, 95% BCI: 0.9681-0.9995), and flow accumulation (RR: 0.9586, 95% BCI: 0.9321-0.9816) were negatively associated with O. volvulus nodule prevalence, while soil moisture (RR: 1.0218, 95% BCI: 1.0135-1.0302) was positively associated. The model estimated the number of pre-intervention cases of O. volvulus nodules in Ethiopia to be around 6.48 million (95% BCI: 3.53-13.04 million).

Conclusions/significance: Nodule prevalence distribution was correlated with habitat suitability for vector breeding and associated biting behavior. The modeled pre-intervention prevalence can be used as a guide for determining priorities for elimination mapping in regions of Ethiopia that are currently unmapped, most of which have comparatively low infection prevalence.

Fig 1. Sites and the nodule prevalence measured during Rapid Epidemiological Mapping of Onchocerciasis (REMO) in Ethiopia.

The internal boundaries on the map represents administrative regions. The inset figure shows the histogram of prevalence. The administrative borders are from the Global Administrative Areas (GADM) database (available at: https://gadm.org/maps.html).

Fig 2. Onchocerca volvulus nodule posterior prevalence map in Ethiopia generated from the geostatistical model.

(A) The mean, (B) the lower limit, and (C) the upper limit of O. volvulus nodule prevalence. The prediction interval of the prevalence map was generated from the calculated 95% BCI of fitted posterior prevalence values. The administrative borders are from the Global Administrative Areas (GADM) database (available at: https://gadm.org/maps.html).

Fig 3. Uncertainty in the estimates of O. volvulus nodule prevalence from the model.

(A) Exceedance threshold probability map that shows the posterior predictive probability that the nodule prevalence is greater than 20%. (B) Bivariate map that shows both prevalence and the uncertainty estimates rescaled from 0 to 1. The administrative borders are from the Global Administrative Areas (GADM) database (available at: https://gadm.org/maps.html).

Fig 4. The estimated number of pre-intervention O. volvulus nodule prevalence cases with respect to Ethiopian districts.

(A) The mean pre-intervention cases, (B) their upper and (C) lower limit were aggregated at the district level border. The regional borders are highlighted with thicker lines. The administrative borders are from the Global Administrative Areas (GADM) database (available at: https://gadm.org/maps.html).

Fig 5. The relationship between the predicted posterior mean prevalence with the significant environmental covariates.

The curve was fitted using the gam smoothing function available in the ggplot2 package for the purpose of visualization. The shaded region around each curve represents the 95% confidence interval. Flow accumulation had a range of high magnitude compared to other covariates (values ranged from 0 to 100418). Thus, this variable was rescaled from 0 to 100 to make its range comparable with other variables.