On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data

Abstract

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.

Bidirectional Causality between Spreading COVID-19 and Individual Mobilisation with Consumption Motives across Prefectural Borders in Japan

“…Visitors to all six sites from the other prefectures were lesser than domestic visitors ( Figure 2 ). Notably, similar to the previous findings regarding the night-time population in Tokyo [ 19 ], the onset period of decreasing visitors to all six sites in the three metropolitan regions was synchronised with the onset of increasing NCCC in these prefectures and was prior to the announcement of the governmental restriction measures “state of emergency (stay-at-home order)” ( Figure 1 and Figure 2 ).…”

“…Individuals move/ambulate for specific purposes, such as work, education, consumption, or recreation. In Japan, the first “state of emergency” (a stay-at-home order targeted at reducing individual mobilisation at night-time) could not decrease the night-time population in the epicentre regions [ 19 ]. Notably, despite the severe governmental social restriction measures under the state of emergency, the fifth wave of the COVID-19 pandemic, due to the Delta variant, occurred, and the contribution of the mobilising population in Tokyo to the reproduction number was inconsistent [ 20 ].…”

“…Therefore, to sufficiently prevent the spread of COVID-19 without socioeconomic and/or sociopsychological deteriorations, we must identify the specific social contact patterns which play crucial roles in the spread of SARS-CoV-2. It is established that the interpretation of the social contact surveys is complicated compared with a geographic analysis of COVID-19 since social contact behaviours were also drastically affected by personal factors due to the progression of the pandemic itself and the governmental social restricting measures [ 19 , 20 , 21 , 22 ].…”

Associations between components of household expenditures and the rate of change in the number of new confirmed cases of COVID-19 in Japan: Time-series analysis

“…To capture such an effect, it is necessary to estimate the local infectiousness of infected individuals in each region, such as the regional time-varying reproduction number, and then evaluate the associations between fluctuations in the measure of local infectiousness of infected individuals and the components of household expenditures in the region. To the best of our knowledge, this type of regional analysis has been conducted only with mobility data in Japan [ 35 , 36 ]. This issue is important because the local infectiousness of household expenditures is likely to depend on population density, which varies substantially across regions in Japan.…”

COVID-19 forecasting using new viral variants and vaccination effectiveness models

“…In addition, in the analysis of Tokyo, Osaka, and Aichi, the number of tweets and population at night were considered, which are potentially related to changes in public behavior. The effectiveness of the latter can be reported in [38] . The breakdown and definition of each dataset are listed in Table 1 .…”