Open AccessOn-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data
Author(s) -
Miharu Nakanishi,
Ryosuke Shibasaki,
Syudo Yamasaki,
Satoshi Miyazawa,
Satoshi Usami,
Hiroshi Nishiura,
Atsushi Nishida
Publication year - 2021
Publication title -
jmir mhealth and uhealth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.356
H-Index - 50
ISSN - 2291-5222
DOI - 10.2196/27342
Subject(s) - population , demography , metropolitan area , pandemic , public health , gerontology , medicine , geography , statistics , covid-19 , mathematics , sociology , infectious disease (medical specialty) , disease , pathology , nursing
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.