tetano
Editor, Senior Moderator
Sci Rep
. 2020 Oct 29;10(1):18680.
doi: 10.1038/s41598-020-75771-6.
Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information
Shohei Hisada[SUP] 1 [/SUP], Taichi Murayama[SUP] 1 [/SUP], Kota Tsubouchi[SUP] 2 [/SUP], Sumio Fujita[SUP] 2 [/SUP], Shuntaro Yada[SUP] 1 [/SUP], Shoko Wakamiya[SUP] 1 [/SUP], Eiji Aramaki[SUP] 3 [/SUP]
Affiliations
Abstract
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.
. 2020 Oct 29;10(1):18680.
doi: 10.1038/s41598-020-75771-6.
Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information
Shohei Hisada[SUP] 1 [/SUP], Taichi Murayama[SUP] 1 [/SUP], Kota Tsubouchi[SUP] 2 [/SUP], Sumio Fujita[SUP] 2 [/SUP], Shuntaro Yada[SUP] 1 [/SUP], Shoko Wakamiya[SUP] 1 [/SUP], Eiji Aramaki[SUP] 3 [/SUP]
Affiliations
- PMID: 33122686
- DOI: 10.1038/s41598-020-75771-6
Abstract
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.