Syndromic surveillance using search query logs and user location information from smartphones against COVID-19 clusters in Japan
This work addresses syndromic surveillance for public health officials, but it is incremental as it builds on existing digital surveillance methods with limited accuracy.
The study tackled early detection of COVID-19 clusters in Japan by using web search query logs and smartphone location data to identify users suspicious of infection, finding that this approach was feasible in the early stages but became biased after public announcements.
[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.