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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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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. |
format | Online Article Text |
id | pubmed-7596075 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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