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On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data
Miharu Nakanishi1,2, Ryosuke Shibasaki3, Syudo Yamasaki1
1Research Center for Social Science & Medicine, Tokyo Metopolitan Institute of Medical Science, Setagaya-ku, Tokyo, Japan.
JMIR Mhealth and Uhealth
|April 22, 2021
Summary
Nighttime population surges in Tokyo preceded increases in COVID-19 cases. Public health measures should anticipate epidemic trends using mobility data for effective intervention.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- Assessing human behavior's role in COVID-19 transmission is crucial for effective public health strategies.
- Tokyo implemented measures to curb on-site dining during the second COVID-19 wave in August 2020.
Purpose of the Study:
- To investigate the relationship between nighttime population density, COVID-19 spread, and public health interventions in Tokyo.
- To understand how mobility patterns influenced infection dynamics during the pandemic.
Main Methods:
- Utilized mobile phone location data to estimate nighttime populations (10 PM-midnight) in seven Tokyo areas.
- Distinguished on-site dining, work, and home behaviors using mobile phone trajectories.
- Analyzed weekly mobility and infection data from March to November 2020 with vector autoregression.
Main Results:
- Increased nighttime population volume correlated with a rise in COVID-19 symptom onsets within one week.
- A significant increase in the effective reproduction number was observed three weeks after nighttime population surges.
- Nighttime population volume decreased significantly following announcements of declining case numbers.
- Social measures targeting restaurants and bars showed no significant association with nighttime population volume.
Conclusions:
- Nighttime population increases followed reported decreases in COVID-19 incidence.
- Social distancing measures should be proactively planned based on mobility data to preemptively manage epidemic surges.
- Timely analysis of mobility data is essential for effective public health planning and intervention.
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