On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data

Miharu Nakanishi; Ryosuke Shibasaki; Syudo Yamasaki; Satoshi Miyazawa; Satoshi Usami; Hiroshi Nishiura; Atsushi Nishida

doi:10.2196/27342

On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data

JMIR Mhealth Uhealth. 2021 May 11;9(5):e27342. doi: 10.2196/27342.

Authors

Miharu Nakanishi^{1

2}, Ryosuke Shibasaki³, Syudo Yamasaki¹, Satoshi Miyazawa⁴, Satoshi Usami⁵, Hiroshi Nishiura⁶, Atsushi Nishida^{1

7}

Affiliations

¹ Research Center for Social Science & Medicine, Tokyo Metopolitan Institute of Medical Science, Setagaya-ku, Tokyo, Japan.
² Department of Psychiatric Nursing, Tohoku University Graduate School of Medicine, Sendai-shi, Miyagi, Japan.
³ Division of Environmental Studies, Department of Socio-Cultural Environmental Studies, The University of Tokyo, Kashiwa-shi,Chiba, Japan.
⁴ Technology Department, LocationMind Inc, Chiyoda-ku, Tokyo, Japan.
⁵ Center for Research and Development of Higher Education, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
⁶ School of Public Health, Kyoto University, Kyoto-shi, Kyoto, Japan.
⁷ Tokyo Center for Infectious Disease Control and Prevention, Shinjuku-ku, Tokyo, Japan.

PMID: 33886486
PMCID: PMC8115398
DOI: 10.2196/27342

Abstract

Background: During the second wave of COVID-19 in August 2020, the Tokyo Metropolitan Government implemented public health and social measures 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: The aim of this study was to investigate the association between nighttime population volumes, the COVID-19 epidemic, and the implementation of public health and social measures in Tokyo.

Methods: We used mobile phone location data to estimate populations between 10 PM and midnight 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 March 1 to November 14, 2020, were analyzed using a vector autoregression model.

Results: An increase in the number of symptom onsets was observed 1 week after the nighttime population volume increased (coefficient=0.60, 95% CI 0.28 to 0.92). The effective reproduction number significantly increased 3 weeks after the nighttime population volume increased (coefficient=1.30, 95% CI 0.72 to 1.89). The nighttime population volume increased significantly following reports of decreasing numbers of confirmed cases (coefficient=-0.44, 95% CI -0.73 to -0.15). Implementation of social measures to restaurants and bars was not significantly associated with nighttime population volume (coefficient=0.004, 95% CI -0.07 to 0.08).

Conclusions: The nighttime population started to increase after decreasing incidence of COVID-19 was announced. Considering time lags between infection and behavior changes, social measures should be planned in advance of the surge of an epidemic, sufficiently informed by mobility data.

Keywords: COVID-19; infectious disease; location; mobile phone; mobility; mobility data; on-site dining; protection; public health; public health and social measures; time series; transmission.

©Miharu Nakanishi, Ryosuke Shibasaki, Syudo Yamasaki, Satoshi Miyazawa, Satoshi Usami, Hiroshi Nishiura, Atsushi Nishida. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 11.05.2021.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19*
Cell Phone*
Humans
Pandemics
SARS-CoV-2
Tokyo / epidemiology