On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data.
On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data.
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COVID-19 大流行期间东京的现场餐饮：使用手机位置数据进行时间序列分析。
DOI：
10.2196/27342
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发表时间：
2021-05-11
影响因子：
5
通讯作者：
Nishida A
中科院分区：
文献类型：
--
作者：
Nakanishi M;Shibasaki R;Yamasaki S;Miyazawa S;Usami S;Nishiura H;Nishida A
于二零二零年八月第二波COVID-19疫情期间，东京都政府实施公共卫生及社会措施以减少现场用餐。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实现减少和确定推动社会动态变化的因素至关重要。本研究的目的是调查夜间人口数量、COVID-19疫情以及东京公共卫生和社会措施的实施之间的关系。我们使用移动的电话定位数据来估计东京七个大都市地区晚上10点到午夜之间的人口。移动的手机轨迹被用来区分和提取留在工作和呆在家里的行为的现场用餐。获得了新病例和症状发作的数量。使用向量自回归模型分析了2020年3月1日至11月14日的每周流动性和感染数据。夜间人群数量增加后1周观察到症状发作次数增加（系数=0.60，95% CI 0.28 - 0.92）。夜间种群数量增加后3周，有效繁殖数显著增加（系数=1.30，95% CI 0.72 ~ 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）。对餐馆和酒吧实施社会措施与夜间人口数量无显著相关性（系数=0.004，95%CI-0.07至0.08）。在宣布COVID-19发病率下降后，夜间人口开始增加。考虑到感染和行为改变之间的时间滞后，应该在流行病激增之前规划社会措施，充分了解流动数据。
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.
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DOI：
10.1016/s2589-7500(20)30243-0
发表时间：
2020-12
期刊：
The Lancet. Digital health
影响因子：
--
作者：
Pullano G;Valdano E;Scarpa N;Rubrichi S;Colizza V
通讯作者：
Colizza V
DOI：
10.1016/s1473-3099(20)30833-1
发表时间：
2021-03
期刊：
The Lancet. Infectious diseases
影响因子：
--
作者：
Ng OT;Marimuthu K;Koh V;Pang J;Linn KZ;Sun J;De Wang L;Chia WN;Tiu C;Chan M;Ling LM;Vasoo S;Abdad MY;Chia PY;Lee TH;Lin RJ;Sadarangani SP;Chen MI;Said Z;Kurupatham L;Pung R;Wang LF;Cook AR;Leo YS;Lee VJ
通讯作者：
Lee VJ
影响因子：
1.9
作者：
Cho, Sang-Wook (stanley)
通讯作者：
Cho, Sang-Wook (stanley)
DOI：
10.1073/pnas.2007658117
发表时间：
2020-07-07
影响因子：
11.1
作者：
Bonaccorsi, Giovanni;Pierri, Francesco;Pammolli, Fabio
通讯作者：
Pammolli, Fabio
DOI：
10.1016/s0140-6736(20)32007-9
发表时间：
2020-11-07
期刊：
Lancet (London, England)
影响因子：
--
作者：
Han E;Tan MMJ;Turk E;Sridhar D;Leung GM;Shibuya K;Asgari N;Oh J;García-Basteiro AL;Hanefeld J;Cook AR;Hsu LY;Teo YY;Heymann D;Clark H;McKee M;Legido-Quigley H
通讯作者：
Legido-Quigley H