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Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information

Two clusters of the coronavirus disease 2019 (COVID-19) were confirmed in Hokkaido, Japan, in February 2020. To identify these clusters, this study employed web search query logs of multiple devices and user location information from location-aware mobile devices. We anonymously identified users who...

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Autores principales: Hisada, Shohei, Murayama, Taichi, Tsubouchi, Kota, Fujita, Sumio, Yada, Shuntaro, Wakamiya, Shoko, Aramaki, Eiji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596075/
https://www.ncbi.nlm.nih.gov/pubmed/33122686
http://dx.doi.org/10.1038/s41598-020-75771-6
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author Hisada, Shohei
Murayama, Taichi
Tsubouchi, Kota
Fujita, Sumio
Yada, Shuntaro
Wakamiya, Shoko
Aramaki, Eiji
author_facet Hisada, Shohei
Murayama, Taichi
Tsubouchi, Kota
Fujita, Sumio
Yada, Shuntaro
Wakamiya, Shoko
Aramaki, Eiji
author_sort Hisada, Shohei
collection PubMed
description Two clusters of the coronavirus disease 2019 (COVID-19) were confirmed in Hokkaido, Japan, in February 2020. To identify these clusters, this study employed web search query logs of multiple devices and user location information from location-aware mobile devices. We anonymously identified users who used a web search engine (i.e., Yahoo! JAPAN) to search for COVID-19 or its symptoms. We regarded them as web searchers who were suspicious of their own COVID-19 infection (WSSCI). We extracted the location of WSSCI via a mobile operating system application and compared the spatio-temporal distribution of WSSCI with the actual location of the two known clusters. In the early stage of cluster development, we confirmed several WSSCI. Our approach was accurate in this stage and became biased after a public announcement of the cluster development. When other cluster-related resources, such as detailed population statistics, are not available, the proposed metric can capture hints of emerging clusters.
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spelling pubmed-75960752020-10-30 Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information Hisada, Shohei Murayama, Taichi Tsubouchi, Kota Fujita, Sumio Yada, Shuntaro Wakamiya, Shoko Aramaki, Eiji Sci Rep Article Two clusters of the coronavirus disease 2019 (COVID-19) were confirmed in Hokkaido, Japan, in February 2020. To identify these clusters, this study employed web search query logs of multiple devices and user location information from location-aware mobile devices. We anonymously identified users who used a web search engine (i.e., Yahoo! JAPAN) to search for COVID-19 or its symptoms. We regarded them as web searchers who were suspicious of their own COVID-19 infection (WSSCI). We extracted the location of WSSCI via a mobile operating system application and compared the spatio-temporal distribution of WSSCI with the actual location of the two known clusters. In the early stage of cluster development, we confirmed several WSSCI. Our approach was accurate in this stage and became biased after a public announcement of the cluster development. When other cluster-related resources, such as detailed population statistics, are not available, the proposed metric can capture hints of emerging clusters. Nature Publishing Group UK 2020-10-29 /pmc/articles/PMC7596075/ /pubmed/33122686 http://dx.doi.org/10.1038/s41598-020-75771-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Hisada, Shohei
Murayama, Taichi
Tsubouchi, Kota
Fujita, Sumio
Yada, Shuntaro
Wakamiya, Shoko
Aramaki, Eiji
Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information
title Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information
title_full Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information
title_fullStr Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information
title_full_unstemmed Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information
title_short Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information
title_sort surveillance of early stage covid-19 clusters using search query logs and mobile device-based location information
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596075/
https://www.ncbi.nlm.nih.gov/pubmed/33122686
http://dx.doi.org/10.1038/s41598-020-75771-6
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