Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana

Abstract

Background: Lymphatic Filariasis (LF), a parasitic nematode infection, poses a huge economic burden to affected countries. LF endemicity is localized and its prevalence is spatially heterogeneous. In Ghana, there exists differences in LF prevalence and multiplicity of symptoms in the country's northern and southern parts. Species distribution models (SDMs) have been utilized to explore the suite of risk factors that influence the transmission of LF in these geographically distinct regions.

Methods: Presence-absence records of microfilaria (mf) cases were stratified into northern and southern zones and used to run SDMs, while climate, socioeconomic, and land cover variables provided explanatory information. Generalized Linear Model (GLM), Generalized Boosted Model (GBM), Artificial Neural Network (ANN), Surface Range Envelope (SRE), Multivariate Adaptive Regression Splines (MARS), and Random Forests (RF) algorithms were run for both study zones and also for the entire country for comparison.

Results: Best model quality was obtained with RF and GBM algorithms with the highest Area under the Curve (AUC) of 0.98 and 0.95, respectively. The models predicted high suitable environments for LF transmission in the short grass savanna (northern) and coastal (southern) areas of Ghana. Mainly, land cover and socioeconomic variables such as proximity to inland water bodies and population density uniquely influenced LF transmission in the south. At the same time, poor housing was a distinctive risk factor in the north. Precipitation, temperature, slope, and poverty were common risk factors but with subtle variations in response values, which were confirmed by the countrywide model.

Conclusions: This study has demonstrated that different variable combinations influence the occurrence of lymphatic filariasis in northern and southern Ghana. Thus, an understanding of the geographic distinctness in risk factors is required to inform on the development of area-specific transmission control systems towards LF elimination in Ghana and internationally.

Keywords: Ecological niche modelling; Ensemble modelling; Generalised boosted model (GBM); Lymphatic filariasis; Machine learning; Random forest (RF).

Fig. 1

Map of Ghana showing the districts included in the two study zones, NZ and SZ shaded in grey. (This map was generated by authors with ArcGIS V.10.6 software (ESRI, Redlands, CA, USA) and no permissions are required to publish it)

Fig. 2

mf cases for surveyed communities from 2000 to 2014 (yellow indicates absence and red indicates presence), a) CW, b) NZ and c) SZ Zones. (This map was generated by authors with ArcGIS V.10.6 software (ESRI, Redlands, CA, USA) and no permissions are required to publish it)

Fig. 3

Response Curves of retained variables in the northern zone (NZ). The graphs were created for the RF and GBM species distribution models. (Plots were created by authors with RStudio Version 3.5.3 and no permissions are required to publish it)

Fig. 4

Response Curves of retained variables in the southern zone (SZ). The graphs were created for the RF and GBM species distribution models. (Plots were created by authors with RStudio Version 3.5.3 and no permissions are required to publish it)

Fig. 5

Response Curves of retained variables for the countrywide model (CW). The graphs were created for the RF and GBM species distribution models. (Plots were created by authors with RStudio Version 3.5.3 and no permissions are required to publish it)

Fig. 6

Probability maps of LF occurrence (%) generated with ensemble species distribution models, a) CW dataset and b) coastline exaggerated for cartographic purposes. Only the areas with a probability ≥0.5 are presented. Probabilities of ≥0.8 are highlighted in red shades and represent most likely transmission areas. (This map was generated by authors with ArcGIS V.10.6 software (ESRI, Redlands, CA, USA) and no permissions are required to publish it)

Fig. 7

Probability maps of LF occurrence generated with ensemble species distribution models a) NZ and b) SZ. Only the areas with a probability ≥0.5 are presented. Probabilities of ≥0.8 are highlighted in red shades and represent most likely transmission areas. (This map was generated by authors with ArcGIS V.10.6 software (ESRI, Redlands, CA, USA) and no permissions are required to publish it)