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Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration.

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Bibliographic Details
Title: Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration.
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
Description
ISSN:1091-6490
DOI:10.1073/pnas.2315463120