Optimizing sentinel surveillance in temporal network epidemiology

Abstract To help health policy makers gain response time to mitigate infectious disease threats, it is essential to have an efficient epidemic surveillance. One common method of disease surveillance is to carefully select nodes (sentinels, or sensors) in the network to report outbreaks. One would li...

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Autores principales:	Yuan Bai, Bo Yang, Lijuan Lin, Jose L. Herrera, Zhanwei Du, Petter Holme
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a75489081dac4236af623b2eab3fe6f9
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spelling	oai:doaj.org-article:a75489081dac4236af623b2eab3fe6f92021-12-02T15:05:20ZOptimizing sentinel surveillance in temporal network epidemiology10.1038/s41598-017-03868-62045-2322https://doaj.org/article/a75489081dac4236af623b2eab3fe6f92017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03868-6https://doaj.org/toc/2045-2322Abstract To help health policy makers gain response time to mitigate infectious disease threats, it is essential to have an efficient epidemic surveillance. One common method of disease surveillance is to carefully select nodes (sentinels, or sensors) in the network to report outbreaks. One would like to choose sentinels so that they discover the outbreak as early as possible. The optimal choice of sentinels depends on the network structure. Studies have addressed this problem for static networks, but this is a first step study to explore designing surveillance systems for early detection on temporal networks. This paper is based on the idea that vaccination strategies can serve as a method to identify sentinels. The vaccination problem is a related question that is much more well studied for temporal networks. To assess the ability to detect epidemic outbreaks early, we calculate the time difference (lead time) between the surveillance set and whole population in reaching 1% prevalence. We find that the optimal selection of sentinels depends on both the network’s temporal structures and the infection probability of the disease. We find that, for a mild infectious disease (low infection probability) on a temporal network in relation to potential disease spreading (the Prostitution network), the strategy of selecting latest contacts of random individuals provide the most amount of lead time. And for a more uniform, synthetic network with community structure the strategy of selecting frequent contacts of random individuals provide the most amount of lead time.Yuan BaiBo YangLijuan LinJose L. HerreraZhanwei DuPetter HolmeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuan Bai Bo Yang Lijuan Lin Jose L. Herrera Zhanwei Du Petter Holme Optimizing sentinel surveillance in temporal network epidemiology
description	Abstract To help health policy makers gain response time to mitigate infectious disease threats, it is essential to have an efficient epidemic surveillance. One common method of disease surveillance is to carefully select nodes (sentinels, or sensors) in the network to report outbreaks. One would like to choose sentinels so that they discover the outbreak as early as possible. The optimal choice of sentinels depends on the network structure. Studies have addressed this problem for static networks, but this is a first step study to explore designing surveillance systems for early detection on temporal networks. This paper is based on the idea that vaccination strategies can serve as a method to identify sentinels. The vaccination problem is a related question that is much more well studied for temporal networks. To assess the ability to detect epidemic outbreaks early, we calculate the time difference (lead time) between the surveillance set and whole population in reaching 1% prevalence. We find that the optimal selection of sentinels depends on both the network’s temporal structures and the infection probability of the disease. We find that, for a mild infectious disease (low infection probability) on a temporal network in relation to potential disease spreading (the Prostitution network), the strategy of selecting latest contacts of random individuals provide the most amount of lead time. And for a more uniform, synthetic network with community structure the strategy of selecting frequent contacts of random individuals provide the most amount of lead time.
format	article
author	Yuan Bai Bo Yang Lijuan Lin Jose L. Herrera Zhanwei Du Petter Holme
author_facet	Yuan Bai Bo Yang Lijuan Lin Jose L. Herrera Zhanwei Du Petter Holme
author_sort	Yuan Bai
title	Optimizing sentinel surveillance in temporal network epidemiology
title_short	Optimizing sentinel surveillance in temporal network epidemiology
title_full	Optimizing sentinel surveillance in temporal network epidemiology
title_fullStr	Optimizing sentinel surveillance in temporal network epidemiology
title_full_unstemmed	Optimizing sentinel surveillance in temporal network epidemiology
title_sort	optimizing sentinel surveillance in temporal network epidemiology
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/a75489081dac4236af623b2eab3fe6f9
work_keys_str_mv	AT yuanbai optimizingsentinelsurveillanceintemporalnetworkepidemiology AT boyang optimizingsentinelsurveillanceintemporalnetworkepidemiology AT lijuanlin optimizingsentinelsurveillanceintemporalnetworkepidemiology AT joselherrera optimizingsentinelsurveillanceintemporalnetworkepidemiology AT zhanweidu optimizingsentinelsurveillanceintemporalnetworkepidemiology AT petterholme optimizingsentinelsurveillanceintemporalnetworkepidemiology
_version_	1718388851035078656

Optimizing sentinel surveillance in temporal network epidemiology

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