ArticleJMIR mHealth and uHealth2021
On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data.
Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Pooled it
- Revealing spatiotemporal variations in areas potentially linked to COVID-19 spread using fine-grained population data.Scientific reports · 2025Article
- Hotspot analysis of COVID-19 infection in Tokyo based on influx patterns.Scientific reports · 2025Article
- Public holidays increased the transmission of COVID-19 in Japan, 2020-2021: a mathematical modelling study.Epidemiology and health · 2024Article
- Population-Level Immunity for Transient Suppression of COVID-19 Waves in Japan from April 2021 to September 2022.Vaccines · 2023Article
- Projection of COVID-19 Positive Cases Considering Hybrid Immunity: Case Study in Tokyo.Vaccines · 2023Article
- Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures.Health care management science · 2023Article
- Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities.Transportation research interdisciplinary perspectives · 2023Article
- Night-time population consistently explains the transmission dynamics of coronavirus disease 2019 in three megacities in Japan.Frontiers in public health · 2023Article
- Sustaining self-restraint until the middle of the COVID-19 pandemic in Tokyo.Scientific reports · 2022Article
- Estimation of mRNA COVID-19 Vaccination Effectiveness in Tokyo for Omicron Variants BA.2 and BA.5: Effect of Social Behavior.Vaccines · 2022Article
- COVID-19 forecasting using new viral variants and vaccination effectiveness models.Computers in biology and medicine · 2022Article
- Bidirectional Causality between Spreading COVID-19 and Individual Mobilisation with Consumption Motives across Prefectural Borders in Japan.International journal of environmental research and public health · 2022Article
- The association between the dynamics of COVID-19, related measures, and daytime population in Tokyo.Scientific reports · 2022Article
- Analyzing the changing relationship between personal consumption and suicide mortality during COVID-19 pandemic in Japan, using governmental and personal consumption transaction databases.Frontiers in public health · 2022Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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