Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Jan 22, 2021
Date Accepted: Apr 11, 2021
Date Submitted to PubMed: Apr 22, 2021
COVID-19 and On-site Dining in Tokyo: A Time-series Analysis Using Mobile Phone Location Data.
ABSTRACT
Background:
During the second COVID-19 wave in August 2020, the Tokyo Metropolitan Government implemented public health and social measures (PHSMs) to reduce on-site dining. Assessing the associations between human behavior, infection, and social measures is essential to understand achievable reductions in cases and identify the factors driving changes in social dynamics.
Objective:
We investigated the association between night-time populations, the COVID-19 epidemic, and the implementation of PHSMs in Tokyo.
Methods:
We used mobile phone location data to estimate populations between 10–12pm in seven Tokyo metropolitan areas. Mobile phone trajectories were used to distinguish and extract on-site dining from stay-at-work and stay-at-home behaviors. Numbers of new cases and symptom onsets were obtained. Weekly mobility and infection data from 1 March to 14 November 2020 were analyzed using a vector autoregression model.
Results:
An increase in symptom onsets was observed one week after the night-time population increased (coefficient = 0.60, 95% confidence interval [CI] = 0.28, 0.92). The effective reproduction number (R(t)) significantly increased three weeks after the night-time population increased (coefficient = 1.30, 95%CI = 0.72, 1.89). The night-time population increased significantly following reports of decreasing numbers of confirmed cases (coefficient = -0.44, 95%CI = -0.73, -0.15). Implementation of social measures to restaurants and bars was not significantly associated with night-time population (coefficient = 0.004, 95%CI = -0.07, 0.08).
Conclusions:
The night-time population started to increase once a decreasing incidence was announced. Considering time lags between infection and behavior changes, social measures should be planned in advance of the surge of epidemic, sufficiently informed by mobility data.
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