On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data
BackgroundDuring 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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2021-05-01
|
| Series: | JMIR mHealth and uHealth |
| Online Access: | https://mhealth.jmir.org/2021/5/e27342 |
| _version_ | 1798023714489499648 |
|---|---|
| author | Nakanishi, Miharu Shibasaki, Ryosuke Yamasaki, Syudo Miyazawa, Satoshi Usami, Satoshi Nishiura, Hiroshi Nishida, Atsushi |
| author_facet | Nakanishi, Miharu Shibasaki, Ryosuke Yamasaki, Syudo Miyazawa, Satoshi Usami, Satoshi Nishiura, Hiroshi Nishida, Atsushi |
| author_sort | Nakanishi, Miharu |
| collection | DOAJ |
| description | BackgroundDuring 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.
ObjectiveThe 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.
MethodsWe 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.
ResultsAn 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).
ConclusionsThe 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. |
| first_indexed | 2024-04-11T17:50:42Z |
| format | Article |
| id | doaj.art-4074c77a491f423199cd9a35a4390d63 |
| institution | Directory Open Access Journal |
| issn | 2291-5222 |
| language | English |
| last_indexed | 2024-04-11T17:50:42Z |
| publishDate | 2021-05-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR mHealth and uHealth |
| spelling | doaj.art-4074c77a491f423199cd9a35a4390d632022-12-22T04:11:06ZengJMIR PublicationsJMIR mHealth and uHealth2291-52222021-05-0195e2734210.2196/27342On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location DataNakanishi, MiharuShibasaki, RyosukeYamasaki, SyudoMiyazawa, SatoshiUsami, SatoshiNishiura, HiroshiNishida, AtsushiBackgroundDuring 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. ObjectiveThe 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. MethodsWe 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. ResultsAn 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). ConclusionsThe 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.https://mhealth.jmir.org/2021/5/e27342 |
| spellingShingle | Nakanishi, Miharu Shibasaki, Ryosuke Yamasaki, Syudo Miyazawa, Satoshi Usami, Satoshi Nishiura, Hiroshi Nishida, Atsushi On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data JMIR mHealth and uHealth |
| title | On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data |
| title_full | On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data |
| title_fullStr | On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data |
| title_full_unstemmed | On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data |
| title_short | On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data |
| title_sort | on site dining in tokyo during the covid 19 pandemic time series analysis using mobile phone location data |
| url | https://mhealth.jmir.org/2021/5/e27342 |
| work_keys_str_mv | AT nakanishimiharu onsitediningintokyoduringthecovid19pandemictimeseriesanalysisusingmobilephonelocationdata AT shibasakiryosuke onsitediningintokyoduringthecovid19pandemictimeseriesanalysisusingmobilephonelocationdata AT yamasakisyudo onsitediningintokyoduringthecovid19pandemictimeseriesanalysisusingmobilephonelocationdata AT miyazawasatoshi onsitediningintokyoduringthecovid19pandemictimeseriesanalysisusingmobilephonelocationdata AT usamisatoshi onsitediningintokyoduringthecovid19pandemictimeseriesanalysisusingmobilephonelocationdata AT nishiurahiroshi onsitediningintokyoduringthecovid19pandemictimeseriesanalysisusingmobilephonelocationdata AT nishidaatsushi onsitediningintokyoduringthecovid19pandemictimeseriesanalysisusingmobilephonelocationdata |