Mobility Change and COVID-19 in Japan: Mobile Data Analysis of Locations of Infection

Abstract

Background: As the COVID-19 pandemic spread, the Japanese government declared a state of emergency on April 7, 2020 for seven prefectures, and on April 16, 2020 for all prefectures. The Japanese Prime Minister and governors requested people to adopt self-restraint behaviors, including working from home and refraining from visiting nightlife spots. However, the effectiveness of the mobility change due to such requests in reducing the spread of COVID-19 has been little investigated. The present study examined the association of the mobility change in working, nightlife, and residential places and the COVID-19 outbreaks in Tokyo, Osaka, and Nagoya metropolitan areas in Japan. Methods: First, we calculated the daily mobility change in working, nightlife, and residential places compared to the mobility before the outbreak using mobile device data. Second, we estimated the sensitivity of mobility changes to the reproduction number by generalized least squares. Results: Mobility change had already started in March, 2020. However, mobility reduction in nightlife places was particularly significant due to the state of emergency declaration. Although the mobility in each place type was associated with the COVID-19 outbreak, the mobility changes in nightlife places were more significantly associated with the outbreak than those in the other place types. There were regional differences in intensity of sensitivity among each metropolitan area. Conclusions: Our findings indicated the effectiveness of the mobility changes, particularly in nightlife places, in reducing the outbreak of COVID-19.

The impact of temperature on the transmission potential and virulence of COVID-19 in Tokyo, Japan

“…The exposure-response relationships of population mobilities and meteorological factors with R t are consistent with previous works [17, 24, 25]. For example, the residual and workplace mobility change were not slightly related to the fluctuation of R t while the recreation mobility change was significant.…”

“…For example, the residual and workplace mobility change were not slightly related to the fluctuation of while the recreation mobility change was significant. This relationship in Tokyo were reported in the previous study [17]. The exposure-response outcome of solar radiation indicated a significant negative correlation while that of wind speed shows a significant positive correlation (Supplementary Table S1), and similar relationships have been reported elsewhere [24,25].…”

“…Seven df per 380 days to time were chosen. In addition, R t would be also influenced by the suppression or mitigation strategies, and other social behavioral changes due to increase in individual awareness of infection [17]. Therefore, we used mobility data, specifically classified into recreation, work, and residual place based on Google mobility data, assuming the three types of places as major possible sites of infection as the variables in the model which involved in some non-pharmaceutical interventions.…”

Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

“…Similarly, Arimura et al demonstrated that the declaration reduced human mobility by up to nearly 90% in Sapporo City (Hokkaido Prefecture), depending on the place and time frame of a day [ 20 ]. Nagata et al, who tracked human mobility in Tokyo, Osaka, and Aichi Prefectures using smartphone data, pointed out that the reduction in human mobility started before the first declaration of the state of emergency, and that the degree of reduction varied from place to place [ 21 ]. Furthermore, Kajitani et al found that a 20–35% reduction in mobility may have been necessary to hold back the pandemic (i.e., to reduce the effective reproduction number to one or less) in the situation during the first wave of the pandemic in the business and commercial districts of nine prefectures including Tokyo [ 22 ].…”

Relationship of Test Positivity Rates with COVID-19 Epidemic Dynamics

“…An Rt value >1 indicates that the number of cases is increasing, whereas an Rt of <1 suggests that the infection spread is slowing. Prediction of Rt has been extensively studied using human mobility data to forecast the number of future cases and to develop an effective strategy for public health measures [ 7 , 8 ]. There are also other studies that used machine learning techniques or biological data for the prediction of Rt [ 9 , 10 ].…”