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11 May 2021
Miharu Nakanishi (Research Center for Social Science & Medicine Sciences) et al. published an article entitled “On-site dining in Tokyo during the COVID-19 pandemic: a time-series analysis using mobile phone location data.” in JMIR mHealth and uHealth

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On-site dining in Tokyo during the COVID-19 pandemic: a time-series analysis using mobile phone location data.

<Title of the paper>
"On-site dining in Tokyo during the COVID-19 pandemic: a time-series analysis using mobile phone location data."
<Journal>
JMIR mHealth and uHealth, 2021; 9(5): e27342.
DOI:10.2196/27342
URL:https://mhealth.jmir.org/2021/5/e27342

The COVID-19 global pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), requires nations to implement public health and social measures (PHSMs), strategically focusing on mobility restrictions. SARS-CoV-2 is transmitted primarily via respiratory droplets via close face-to-face contact. As the infection can be spread by asymptomatic and pre-symptomatic carriers, PHSMs are warranted that target physical distancing and minimizing verbal interactions. However, it has remained unclear how successfully the PHSMs could be implemented to reduce specific behaviors at high risk of infection, i.e. on-site dining.

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 March 1st to November 14th, 2020 were analyzed. 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).

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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