Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys

The decision when to stop an intervention is a critical component of parasite elimination programmes, but reliance on surveillance data alone can be inaccurate. Here, Michael et al. combine parasite transmission model predictions with disease survey data to more reliably determine when interventions...

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Autores principales:	Edwin Michael, Morgan E. Smith, Moses N. Katabarwa, Edson Byamukama, Emily Griswold, Peace Habomugisha, Thomson Lakwo, Edridah Tukahebwa, Emmanuel S. Miri, Abel Eigege, Evelyn Ngige, Thomas R. Unnasch, Frank O. Richards
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/a91fddbacb9e4eaeaabd79e3dbbc8e08
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spelling	oai:doaj.org-article:a91fddbacb9e4eaeaabd79e3dbbc8e082021-12-02T15:33:56ZSubstantiating freedom from parasitic infection by combining transmission model predictions with disease surveys10.1038/s41467-018-06657-52041-1723https://doaj.org/article/a91fddbacb9e4eaeaabd79e3dbbc8e082018-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-06657-5https://doaj.org/toc/2041-1723The decision when to stop an intervention is a critical component of parasite elimination programmes, but reliance on surveillance data alone can be inaccurate. Here, Michael et al. combine parasite transmission model predictions with disease survey data to more reliably determine when interventions can be stopped.Edwin MichaelMorgan E. SmithMoses N. KatabarwaEdson ByamukamaEmily GriswoldPeace HabomugishaThomson LakwoEdridah TukahebwaEmmanuel S. MiriAbel EigegeEvelyn NgigeThomas R. UnnaschFrank O. RichardsNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-13 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Edwin Michael Morgan E. Smith Moses N. Katabarwa Edson Byamukama Emily Griswold Peace Habomugisha Thomson Lakwo Edridah Tukahebwa Emmanuel S. Miri Abel Eigege Evelyn Ngige Thomas R. Unnasch Frank O. Richards Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys
description	The decision when to stop an intervention is a critical component of parasite elimination programmes, but reliance on surveillance data alone can be inaccurate. Here, Michael et al. combine parasite transmission model predictions with disease survey data to more reliably determine when interventions can be stopped.
format	article
author	Edwin Michael Morgan E. Smith Moses N. Katabarwa Edson Byamukama Emily Griswold Peace Habomugisha Thomson Lakwo Edridah Tukahebwa Emmanuel S. Miri Abel Eigege Evelyn Ngige Thomas R. Unnasch Frank O. Richards
author_facet	Edwin Michael Morgan E. Smith Moses N. Katabarwa Edson Byamukama Emily Griswold Peace Habomugisha Thomson Lakwo Edridah Tukahebwa Emmanuel S. Miri Abel Eigege Evelyn Ngige Thomas R. Unnasch Frank O. Richards
author_sort	Edwin Michael
title	Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys
title_short	Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys
title_full	Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys
title_fullStr	Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys
title_full_unstemmed	Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys
title_sort	substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/a91fddbacb9e4eaeaabd79e3dbbc8e08
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Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys

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