Research Center for Social Science & Medicine, Tokyo Metopolitan Institute of Medical Science, Setagaya-ku, Tokyo, Japan.
Department of Psychiatric Nursing, Tohoku University Graduate School of Medicine, Sendai-shi, Miyagi, Japan.
JMIR Mhealth Uhealth. 2021 May 11;9(5):e27342. doi: 10.2196/27342.
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.
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.
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.
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).
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.
在 2020 年 8 月 COVID-19 的第二波疫情中，东京都政府实施了公共卫生和社会措施，以减少现场就餐。评估人类行为、感染和社会措施之间的关联对于了解病例的可减少程度以及确定推动社会动态变化的因素至关重要。
本研究旨在调查东京夜间人口数量、COVID-19 疫情和公共卫生与社会措施实施之间的关联。
我们使用手机位置数据估计东京都七个地区每晚 10 点至午夜的人口数量。使用手机轨迹来区分和提取现场就餐与留在工作场所和家中的行为。获得了新发病例和症状出现的数量。使用向量自回归模型分析了 2020 年 3 月 1 日至 11 月 14 日的每周流动和感染数据。
在夜间人口数量增加后 1 周观察到症状出现数量增加（系数=0.60，95%CI 0.28 至 0.92）。在夜间人口数量增加后 3 周，有效繁殖数显著增加（系数=1.30，95%CI 0.72 至 1.89）。在报告确诊病例数量减少后，夜间人口数量显著增加（系数=-0.44，95%CI -0.73 至 -0.15）。针对餐馆和酒吧实施的社会措施与夜间人口数量无显著关联（系数=0.004，95%CI 0.07 至 0.08）。
在宣布 COVID-19 发病率下降后，夜间人口数量开始增加。考虑到感染和行为变化之间的时间滞后，应在疫情激增之前提前计划社会措施，并充分利用流动数据提供信息。
