Optimizing provider recruitment for influenza surveillance networks.

The increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new, more advanced methods for surveillance, early detection, and decision-making. Here, we demonstrate that a new method for optimizing surveillance networks can improve the quality of epide...

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Autores principales:	Samuel V Scarpino, Nedialko B Dimitrov, Lauren Ancel Meyers
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/796e657cb8324c0f829a3b7cdec8e464
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id	oai:doaj.org-article:796e657cb8324c0f829a3b7cdec8e464
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spelling	oai:doaj.org-article:796e657cb8324c0f829a3b7cdec8e4642021-11-18T05:51:24ZOptimizing provider recruitment for influenza surveillance networks.1553-734X1553-735810.1371/journal.pcbi.1002472https://doaj.org/article/796e657cb8324c0f829a3b7cdec8e4642012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22511860/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new, more advanced methods for surveillance, early detection, and decision-making. Here, we demonstrate that a new method for optimizing surveillance networks can improve the quality of epidemiological information produced by typical provider-based networks. Using past surveillance and Internet search data, it determines the precise locations where providers should be enrolled. When applied to redesigning the provider-based, influenza-like-illness surveillance network (ILINet) for the state of Texas, the method identifies networks that are expected to significantly outperform the existing network with far fewer providers. This optimized network avoids informational redundancies and is thereby more effective than networks designed by conventional methods and a recently published algorithm based on maximizing population coverage. We show further that Google Flu Trends data, when incorporated into a network as a virtual provider, can enhance but not replace traditional surveillance methods.Samuel V ScarpinoNedialko B DimitrovLauren Ancel MeyersPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 4, p e1002472 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Samuel V Scarpino Nedialko B Dimitrov Lauren Ancel Meyers Optimizing provider recruitment for influenza surveillance networks.
description	The increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new, more advanced methods for surveillance, early detection, and decision-making. Here, we demonstrate that a new method for optimizing surveillance networks can improve the quality of epidemiological information produced by typical provider-based networks. Using past surveillance and Internet search data, it determines the precise locations where providers should be enrolled. When applied to redesigning the provider-based, influenza-like-illness surveillance network (ILINet) for the state of Texas, the method identifies networks that are expected to significantly outperform the existing network with far fewer providers. This optimized network avoids informational redundancies and is thereby more effective than networks designed by conventional methods and a recently published algorithm based on maximizing population coverage. We show further that Google Flu Trends data, when incorporated into a network as a virtual provider, can enhance but not replace traditional surveillance methods.
format	article
author	Samuel V Scarpino Nedialko B Dimitrov Lauren Ancel Meyers
author_facet	Samuel V Scarpino Nedialko B Dimitrov Lauren Ancel Meyers
author_sort	Samuel V Scarpino
title	Optimizing provider recruitment for influenza surveillance networks.
title_short	Optimizing provider recruitment for influenza surveillance networks.
title_full	Optimizing provider recruitment for influenza surveillance networks.
title_fullStr	Optimizing provider recruitment for influenza surveillance networks.
title_full_unstemmed	Optimizing provider recruitment for influenza surveillance networks.
title_sort	optimizing provider recruitment for influenza surveillance networks.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/796e657cb8324c0f829a3b7cdec8e464
work_keys_str_mv	AT samuelvscarpino optimizingproviderrecruitmentforinfluenzasurveillancenetworks AT nedialkobdimitrov optimizingproviderrecruitmentforinfluenzasurveillancenetworks AT laurenancelmeyers optimizingproviderrecruitmentforinfluenzasurveillancenetworks
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Optimizing provider recruitment for influenza surveillance networks.

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