論 文Papers

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Syndromic surveillance using search query logs and user location information from smartphones against COVID-19 clusters in Japan

Shohei Hisada(NAIST), Taichi Murayama(NAIST), Kota Tsubouchi, Sumio Fujita, Shuntaro Yada(NAIST), Shoko Wakamiya(NAIST), Eiji Aramaki(NAIST)

arXiv, 2020/4

Category:

情報検索 (Information Retrieval) データサイエンス (Data Science)

Abstract:
[Background] Two clusters of coronavirus disease 2019 (COVID-19) were confirmed in Hokkaido, Japan in February 2020. To capture the clusters, this study employs Web search query logs and user location information from smartphones. [Material and Methods] First, we anonymously identified smartphone users who used a Web search engine (Yahoo! JAPAN Search) for the COVID-19 or its symptoms via its companion application for smartphones (Yahoo Japan App). We regard these searchers as Web searchers who are suspicious of their own COVID-19 infection (WSSCI) Second, we extracted the location of the WSSCI via the smartphone application. The spatio-temporal distribution of the number of WSSCI are compared with the actual location of the known two clusters. [Result and Discussion] Before the early stage of the cluster development, we could confirm several WSSCI, which demonstrated the basic feasibility of our WSSCI-based approach. However, it is accurate only in the early stage, and it was biased after the public announcement of the cluster development. For the case where the other cluster-related resources, such as fine-grained population statistics, are not available, the proposed metric would be helpful to catch the hint of emerging clusters.
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