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.
Lymphatic Filariasis (LF), a parasitic nematode infection, often resulting in disability poses huge economic burden to affected countries. To meet eradication deadlines in line with the global Neglected Tropical Diseases elimination and health system strengthening goals, novel strategies are needed to complement existing approaches. LF endemicity is localized and prevalence, spatially heterogeneous. Species distribution models (SDMs) can help identify subtle differences in risk factors that influences the transmission of LF in geographically distinct regions. Thus in this contribution, presence absence records of microfilaria (m)f in Ghana were stratified into Northern and Southern Zones and used to run SDMs, whilst climate, socioeconomic and land coverage variables provided explanation information. GLM (Generalized Linear model), GBM (Generalized Boosted Model), ANN (Artificial Neural Network), SRE (Surface Range Envelope), MARS (Multivariate Adaptive Regression Splines) and RF (Random Forests) algorithms were run for both study zones and also for the entire country for the purpose of comparison. Best model quality was obtained with RF and GBM algorithms with highest AUC between 0.98 and 0.95, respectively. The models predicted high suitable environments for LF transmission in the short grass savanna areas in the northern and along the coastal southern parts of Ghana. Mainly, land cover and socioeconomic variables such as, proximity to inland waterbodies and population density uniquely influenced LF transmission in the South while poor housing was a distinctive risk factors in the North. Precipitation, temperature, slope and poverty were common risk factors but with subtle variations in response values, which was confirmed by the countrywide model. This study has demonstrated that, an understanding of the geographic distinctness in risk factors is required to inform the development of area-specific transmission control systems towards LF elimination in Ghana and internationally.
Lymphatic filariasis (LF) is a leading cause of disability worldwide and one of the most crippling and stigmatizing tropical diseases. LF transmission is widespread throughout regions of West Africa, coastal and south-eastern Africa, East and South-east Asia, South western India, Western Pacific and parts of South and Central America. The disease manifests as disfiguring pathology caused by microfilariae larvae damage to lymph vessels and nodes. LF is spread by mosquitoes that have been infected with filarial nematode larvae and about a billion people in 52 countries are thought to be at risk of contracting the disease on a global scale. Complex immune responses to filaria and their endosymbionts cause the pathologies associated with lymphatic filariasis. Several studies show that non-climatic factors that may be responsible for LF transmission at the micro level include environmental, social, economic, and demographic factors. Currently, the infection is controlled by mass drug administration regimens, vector control strategies and management of morbidities. This review discusses the ecological drivers of lymphatic filariasis transmissions in endemic hotspots.
Lymphatic filariasis (LF) is a public health menace, especially in developing countries. A periodic review of mass drug administration (MDA) performance is critical to monitoring elimination progress. However, investigating the spatial pattern of LF with respect to MDA intervention is yet to be documented. This is essential to appreciating the transmission dynamics across LF-endemic communities and how it is spatially impacted by MDA programs. The aim of this study was to map and explore the spatial variation and hotspots of LF infection among endemic communities and evaluate the impact of the MDA intervention program on its spatial pattern in Ghana. Relative risks, clustering and clusters, prevalence odds ratios, and their confidence intervals were studied with community-level LF data prior to intervention and post intervention periods. The overall risk of LF infection was 0.12% and 0.02% before and after MDA, respectively, suggesting reduced transmission. Using empirical Bayesian smoothing to map the relative risk, a substantial variation in the spatial distribution of the relative risk of LF among endemic communities was observed. Most of the excess prevalence communities were unexpectedly visible even after years of MDA. The Empirical Bayesian Moran’s Index for global clustering showed a reduction in clustering of LF prevalence after MDA with IM = 0.455 and 0.119 for before and after MDA, respectively. Furthermore, examining risks associated with ecological zones, it was observed that the Guinea Savannah and the Transition Zone were the most vulnerable zones for LF infection with prevalence odds ratios 18.70- and 13.20-fold higher than in the reference Moist evergreen zone, respectively. We observed a drastic reduction in risk in the Wet evergreen zone after MDA, while the Guinea Savannah sustained high levels of risk even after MDA. These findings should prompt public health officials to adopt stratified cluster sampling in LF-endemic regions to monitor the rate and density of microfilaria.
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