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

BMC Public Health

Alexander Kwarteng¹  Frank Badu Osei²  Efiba Vidda Senkyire Kwarteng³  Samuel Ato Andam-Akorful³  Da-Costa Boakye Asare³  Alfred Allan Duker³  Jonathan Arthur Quaye-Ballard³ 

[1] Department of Biochemistry and Biotechnology, Kwame Nkrumah University of Science and Technology;Department of Earth Observation Science, University of Twente;Department of Geomatic Engineering, Kwame Nkrumah University of Science and Technology;

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

DOI  :  10.1186/s12889-021-10234-9

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown