Early warning of COVID-19 hotspots using human mobility and web search query data

Takahiro Yabe; Kota Tsubouchi; Yoshihide Sekimoto; Satish V Ukkusuri

doi:10.1016/j.compenvurbsys.2021.101747

Early warning of COVID-19 hotspots using human mobility and web search query data

Comput Environ Urban Syst. 2022 Mar:92:101747. doi: 10.1016/j.compenvurbsys.2021.101747. Epub 2021 Dec 15.

Authors

Takahiro Yabe^{1

2}, Kota Tsubouchi³, Yoshihide Sekimoto⁴, Satish V Ukkusuri¹

Affiliations

¹ Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Avenue, West Lafayette, IN 47907, USA.
² Institute for Data, Systems, and Society, Massachusetts Institute of Technology, 50 Ames St, Cambridge, MA 02142, USA.
³ Yahoo Japan Corporation, Kioi Tower, Tokyo, Garden Terrace Kioicho, 1-3, Kioi-cho, Chiyoda-ku, Tokyo, Japan.
⁴ Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba Meguro-Ku, Tokyo 153-8505, Japan.

PMID: 34931101
PMCID: PMC8673829
DOI: 10.1016/j.compenvurbsys.2021.101747

Abstract

COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventions (e.g., lockdowns) through population density analysis. However, predicting the locations of potential outbreak occurrence is difficult using mobility data alone. Meanwhile, web search queries have been shown to be good predictors of the disease spread. In this study, we utilize a unique dataset of human mobility trajectories (GPS traces) and web search queries with common user identifiers (> 450 K users), to predict COVID-19 hotspot locations beforehand. More specifically, web search query analysis is conducted to identify users with high risk of COVID-19 contraction, and social contact analysis was further performed on the mobility patterns of these users to quantify the risk of an outbreak. Our approach is empirically tested using data collected from users in Tokyo, Japan. We show that by integrating COVID-19 related web search query analytics with social contact networks, we are able to predict COVID-19 hotspot locations 1-2 weeks beforehand, compared to just using social contact indexes or web search data analysis. This study proposes a novel method that can be used in early warning systems for disease outbreak hotspots, which can assist government agencies to prepare effective strategies to prevent further disease spread. Human mobility data and web search query data linked with common IDs are used to predict COVID-19 outbreaks. High risk social contact index captures both the contact density and COVID-19 contraction risks of individuals. Real world data was collected from 200 K individual users in Tokyo during the COVID-19 pandemic. Experiments showed that the index can be used for microscopic outbreak early warning.

Keywords: COVID-19; Epidemics; Human mobility; Web search data.