Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.

Modelling the spatial distributions of human parasite species is crucial to understanding the environmental determinants of infection as well as for guiding the planning of control programmes. Here, we use ecological niche modelling to map the current potential distribution of the macroparasitic dis...

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Main Authors: Hannah Slater, Edwin Michael
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2012-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22359670/pdf/?tool=EBI
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spelling doaj-0f95dec9e96243c99718ca0eca1f4aa12021-03-03T20:30:24ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0172e3220210.1371/journal.pone.0032202Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.Hannah SlaterEdwin MichaelModelling the spatial distributions of human parasite species is crucial to understanding the environmental determinants of infection as well as for guiding the planning of control programmes. Here, we use ecological niche modelling to map the current potential distribution of the macroparasitic disease, lymphatic filariasis (LF), in Africa, and to estimate how future changes in climate and population could affect its spread and burden across the continent. We used 508 community-specific infection presence data collated from the published literature in conjunction with five predictive environmental/climatic and demographic variables, and a maximum entropy niche modelling method to construct the first ecological niche maps describing potential distribution and burden of LF in Africa. We also ran the best-fit model against climate projections made by the HADCM3 and CCCMA models for 2050 under A2a and B2a scenarios to simulate the likely distribution of LF under future climate and population changes. We predict a broad geographic distribution of LF in Africa extending from the west to the east across the middle region of the continent, with high probabilities of occurrence in the Western Africa compared to large areas of medium probability interspersed with smaller areas of high probability in Central and Eastern Africa and in Madagascar. We uncovered complex relationships between predictor ecological niche variables and the probability of LF occurrence. We show for the first time that predicted climate change and population growth will expand both the range and risk of LF infection (and ultimately disease) in an endemic region. We estimate that populations at risk to LF may range from 543 and 804 million currently, and that this could rise to between 1.65 to 1.86 billion in the future depending on the climate scenario used and thresholds applied to signify infection presence.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22359670/pdf/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author Hannah Slater
Edwin Michael
spellingShingle Hannah Slater
Edwin Michael
Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.
PLoS ONE
author_facet Hannah Slater
Edwin Michael
author_sort Hannah Slater
title Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.
title_short Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.
title_full Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.
title_fullStr Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.
title_full_unstemmed Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.
title_sort predicting the current and future potential distributions of lymphatic filariasis in africa using maximum entropy ecological niche modelling.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2012-01-01
description Modelling the spatial distributions of human parasite species is crucial to understanding the environmental determinants of infection as well as for guiding the planning of control programmes. Here, we use ecological niche modelling to map the current potential distribution of the macroparasitic disease, lymphatic filariasis (LF), in Africa, and to estimate how future changes in climate and population could affect its spread and burden across the continent. We used 508 community-specific infection presence data collated from the published literature in conjunction with five predictive environmental/climatic and demographic variables, and a maximum entropy niche modelling method to construct the first ecological niche maps describing potential distribution and burden of LF in Africa. We also ran the best-fit model against climate projections made by the HADCM3 and CCCMA models for 2050 under A2a and B2a scenarios to simulate the likely distribution of LF under future climate and population changes. We predict a broad geographic distribution of LF in Africa extending from the west to the east across the middle region of the continent, with high probabilities of occurrence in the Western Africa compared to large areas of medium probability interspersed with smaller areas of high probability in Central and Eastern Africa and in Madagascar. We uncovered complex relationships between predictor ecological niche variables and the probability of LF occurrence. We show for the first time that predicted climate change and population growth will expand both the range and risk of LF infection (and ultimately disease) in an endemic region. We estimate that populations at risk to LF may range from 543 and 804 million currently, and that this could rise to between 1.65 to 1.86 billion in the future depending on the climate scenario used and thresholds applied to signify infection presence.
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22359670/pdf/?tool=EBI
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