Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration.
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| Title: | Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration. |
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| Authors: | Singer BJ; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA 94304., Coulibaly JT; Unité de Formation et de Recherche Biosciences, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire.; Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, Abidjan, Côte d'Ivoire.; Swiss Tropical and Public Health Institute, Basel, Allschwil 4123 Switzerland.; University of Basel, Basel 4001, Switzerland., Park HJ; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA 94304., Andrews JR; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA 94304., Bogoch II; Department of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada., Lo NC; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA 94304. |
| Source: | Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2024 Jan 09; Vol. 121 (2), pp. e2315463120. Date of Electronic Publication: 2024 Jan 05. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: National Academy of Sciences Country of Publication: United States NLM ID: 7505876 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1091-6490 (Electronic) Linking ISSN: 00278424 NLM ISO Abbreviation: Proc Natl Acad Sci U S A Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| FullText | Text: Availability: 0 |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 38181058 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Singer+BJ%22">Singer BJ</searchLink>; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA 94304.<br /><searchLink fieldCode="AU" term="%22Coulibaly+JT%22">Coulibaly JT</searchLink>; Unité de Formation et de Recherche Biosciences, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire.; Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, Abidjan, Côte d'Ivoire.; Swiss Tropical and Public Health Institute, Basel, Allschwil 4123 Switzerland.; University of Basel, Basel 4001, Switzerland.<br /><searchLink fieldCode="AU" term="%22Park+HJ%22">Park HJ</searchLink>; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA 94304.<br /><searchLink fieldCode="AU" term="%22Andrews+JR%22">Andrews JR</searchLink>; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA 94304.<br /><searchLink fieldCode="AU" term="%22Bogoch+II%22">Bogoch II</searchLink>; Department of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada.<br /><searchLink fieldCode="AU" term="%22Lo+NC%22">Lo NC</searchLink>; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA 94304. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%227505876%22">Proceedings of the National Academy of Sciences of the United States of America</searchLink> [Proc Natl Acad Sci U S A] 2024 Jan 09; Vol. 121 (2), pp. e2315463120. <i>Date of Electronic Publication: </i>2024 Jan 05. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22National+Academy+of+Sciences%22">National Academy of Sciences </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>7505876 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1091-6490 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200278424%22">00278424 </searchLink><i>NLM ISO Abbreviation: </i>Proc Natl Acad Sci U S A <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=38181058 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1073/pnas.2315463120 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: e2315463120 Titles: – TitleFull: Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singer BJ – PersonEntity: Name: NameFull: Coulibaly JT – PersonEntity: Name: NameFull: Park HJ – PersonEntity: Name: NameFull: Andrews JR – PersonEntity: Name: NameFull: Bogoch II – PersonEntity: Name: NameFull: Lo NC IsPartOfRelationships: – BibEntity: Dates: – D: 09 M: 01 Text: 2024 Jan 09 Type: published Y: 2024 Identifiers: – Type: issn-electronic Value: 1091-6490 Numbering: – Type: volume Value: 121 – Type: issue Value: 2 Titles: – TitleFull: Proceedings of the National Academy of Sciences of the United States of America Type: main |
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