Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.
Identifieur interne : 002256 ( PubMed/Corpus ); précédent : 002255; suivant : 002257Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.
Auteurs : Hannah Slater ; Edwin MichaelSource :
- PloS one [ 1932-6203 ] ; 2012.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Africa.
- epidemiology : Elephantiasis, Filarial.
- Climate Change, Ecosystem, Entropy, Forecasting, Humans, Models, Theoretical.
Abstract
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.
DOI: 10.1371/journal.pone.0032202
PubMed: 22359670
Links to Exploration step
pubmed:22359670Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.</title><author><name sortKey="Slater, Hannah" sort="Slater, Hannah" uniqKey="Slater H" first="Hannah" last="Slater">Hannah Slater</name><affiliation><nlm:affiliation>Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Michael, Edwin" sort="Michael, Edwin" uniqKey="Michael E" first="Edwin" last="Michael">Edwin Michael</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:22359670</idno><idno type="pmid">22359670</idno><idno type="doi">10.1371/journal.pone.0032202</idno><idno type="wicri:Area/PubMed/Corpus">002256</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">002256</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.</title><author><name sortKey="Slater, Hannah" sort="Slater, Hannah" uniqKey="Slater H" first="Hannah" last="Slater">Hannah Slater</name><affiliation><nlm:affiliation>Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Michael, Edwin" sort="Michael, Edwin" uniqKey="Michael E" first="Edwin" last="Michael">Edwin Michael</name></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Africa (epidemiology)</term><term>Climate Change</term><term>Ecosystem</term><term>Elephantiasis, Filarial (epidemiology)</term><term>Entropy</term><term>Forecasting</term><term>Humans</term><term>Models, Theoretical</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Africa</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Elephantiasis, Filarial</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Climate Change</term><term>Ecosystem</term><term>Entropy</term><term>Forecasting</term><term>Humans</term><term>Models, Theoretical</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22359670</PMID><DateCreated><Year>2012</Year><Month>02</Month><Day>23</Day></DateCreated><DateCompleted><Year>2012</Year><Month>08</Month><Day>03</Day></DateCompleted><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>7</Volume><Issue>2</Issue><PubDate><Year>2012</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.</ArticleTitle><Pagination><MedlinePgn>e32202</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0032202</ELocationID><Abstract><AbstractText>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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Slater</LastName><ForeName>Hannah</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Michael</LastName><ForeName>Edwin</ForeName><Initials>E</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 AI069387</GrantID><Acronym>AI</Acronym><Agency>NIAID NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01 AI069387-01A1</GrantID><Acronym>AI</Acronym><Agency>NIAID NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>02</Month><Day>16</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Emerg Infect Dis. 2006 Dec;12(12):1822-6</RefSource><PMID Version="1">17326931</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Filaria J. 2007 Nov 29;6:12</RefSource><PMID Version="1">18047646</PMID></CommentsCorrections><CommentsCorrections 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Feb;70(2):105-9</RefSource><PMID Version="1">14993618</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Int J Parasitol. 2003 Aug;33(9):919-31</RefSource><PMID Version="1">12906876</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Ann Trop Med Parasitol. 2002 Dec;96 Suppl 2:S129-32</RefSource><PMID Version="1">12630402</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D000349" MajorTopicYN="N" Type="Geographic">Africa</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D057231" MajorTopicYN="N">Climate Change</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017753" MajorTopicYN="Y">Ecosystem</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004605" MajorTopicYN="N">Elephantiasis, Filarial</DescriptorName><QualifierName UI="Q000453" 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