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

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.

Epidemic forecasting based on mobility patterns: an approach and experimental evaluation on COVID-19 Data

“…Finally, the approach presented in Yabe et al. ( 2022 ) adopts a web search-based approach to identify Covid-19 hotspots, where a user is identified as a high risk user if he/she had more than k (pre-defined threshold value) COVID-19 related web search sessions.…”

“…Finally, the approach presented in Yabe et al. ( 2022 ) presents the experimental evaluation by splitting the geographic area in both and grid cells in Tokyo metropolitan region, thus considering hotspots with specific square shapes.…”

“…Finally, the approach presented in Yabe et al. ( 2022 ) adopts a social contact index (SCI) and a time-lagged cross-correlation analysis to predict the number of new cases.…”

“… 2021 ) pre-defined square regions square regions polynomial Reference (Comito 2021 ) topic-based clustering state borders auto-regression and Naive Bayes Reference (Yabe et al. 2022 ) web search-based grid cells SCI (social contact index) …”

Pseudo-Pflow: Development of Nationwide Synthetic Open Dataset for People Movement Based on Limited Travel Survey and Open Statistical Data

“…Additionally, it is not possible to understand the movements of non-taxi users from the dataset. Some studies [10,37] have used national-level GPS data collected from commercial app users, but it is difficult for many researchers to use such data.…”