Miharu Nakanishi1,2, Ryosuke Shibasaki3, Syudo Yamasaki1, Satoshi Miyazawa4, Satoshi Usami5, Hiroshi Nishiura6, Atsushi Nishida1,7. 1. Research Center for Social Science & Medicine, Tokyo Metopolitan Institute of Medical Science, 2-1-6 Kamikitazawa, Setagaya-ku, Tokyo, JP. 2. Department of Psychiatric Nursing, Tohoku University Graduate School of Medicine, Sendai-shi, Miyagi, JP. 3. Division of Environmental Studies, Department of Socio-Cultural Environmental Studies, the University of Tokyo, Kashiwa-shi,Chiba, JP. 4. Technology Department, LocationMind Inc, Chiyoda-ku, Tokyo, JP. 5. Center for Research and Development of Higher Education, The University of Tokyo, Bunkyo-ku, Tokyo, JP. 6. Kyoto University School of Public Health, Kyoto-shi, Kyoto, JP. 7. Tokyo Center for Infectious Disease Control and Prevention (Tokyo iCDC), Shinjuku-ku, Tokyo, JP.
Abstract
BACKGROUND: During the second COVID-19 wave in August 2020, the Tokyo Metropolitan Government implemented public health and social measures (PHSMs) 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: We investigated the association between night-time populations, the COVID-19 epidemic, and the implementation of PHSMs in Tokyo. METHODS: We used mobile phone location data to estimate populations between 10-12pm 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 1st to November 14th, 2020 were analyzed using a vector autoregression model. RESULTS: An increase in symptom onsets was observed one week after the night-time population increased (coefficient = 0.60, 95% confidence interval [CI] = 0.28, 0.92). The effective reproduction number (R(t)) significantly increased three weeks after the night-time population increased (coefficient = 1.30, 95%CI = 0.72, 1.89). The night-time population increased significantly following reports of decreasing numbers of confirmed cases (coefficient = -0.44, 95%CI = -0.73, -0.15). Implementation of social measures to restaurants and bars was not significantly associated with night-time population (coefficient = 0.004, 95%CI = -0.07, 0.08). CONCLUSIONS: The night-time population started to increase once a decreasing incidence was announced. Considering time lags between infection and behavior changes, social measures should be planned in advance of the surge of epidemic, sufficiently informed by mobility data.
BACKGROUND: During the second COVID-19 wave in August 2020, the Tokyo Metropolitan Government implemented public health and social measures (PHSMs) 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: We investigated the association between night-time populations, the COVID-19 epidemic, and the implementation of PHSMs in Tokyo. METHODS: We used mobile phone location data to estimate populations between 10-12pm 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 1st to November 14th, 2020 were analyzed using a vector autoregression model. RESULTS: An increase in symptom onsets was observed one week after the night-time population increased (coefficient = 0.60, 95% confidence interval [CI] = 0.28, 0.92). The effective reproduction number (R(t)) significantly increased three weeks after the night-time population increased (coefficient = 1.30, 95%CI = 0.72, 1.89). The night-time population increased significantly following reports of decreasing numbers of confirmed cases (coefficient = -0.44, 95%CI = -0.73, -0.15). Implementation of social measures to restaurants and bars was not significantly associated with night-time population (coefficient = 0.004, 95%CI = -0.07, 0.08). CONCLUSIONS: The night-time population started to increase once a decreasing incidence was announced. Considering time lags between infection and behavior changes, social measures should be planned in advance of the surge of epidemic, sufficiently informed by mobility data.
Authors: Yasuhiro Kawano; Ryusuke Matsumoto; Eishi Motomura; Takashi Shiroyama; Motohiro Okada Journal: Int J Environ Res Public Health Date: 2022-07-25 Impact factor: 4.614