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

• DOI: 10.2196/preprints.27342
• Publication Date: 2021-05-11T15:46:37Z
• Authors: Miharu Nakanishi; Ryosuke Shibasaki; Syudo Yamasaki; Satoshi Miyazawa; Satoshi Usami; Hiroshi Nishiura; Atsushi Nishida
• Keywords: Pandemic

<sec> <title>BACKGROUND</title> During the second wave of COVID-19 in August 2020, Tokyo Metropolitan Government implemented public health and social measures to reduce on-site dining. Assessing associations between human behavior, infection, is essential understand achievable reductions cases identify factors driving changes dynamics. </sec> <title>OBJECTIVE</title> The aim this study was investigate association nighttime population volumes, epidemic, implementation Tokyo. <title>METHODS</title> We used mobile phone location data estimate populations 10 PM midnight seven metropolitan areas. Mobile trajectories were distinguish extract dining from stay-at-work stay-at-home behaviors. Numbers new symptom onsets obtained. Weekly mobility infection March 1 November 14, analyzed using a vector autoregression model. <title>RESULTS</title> An increase number observed week after volume increased (coefficient=0.60, 95% CI 0.28 0.92). effective reproduction significantly 3 weeks (coefficient=1.30, 0.72 1.89). following reports decreasing numbers confirmed (coefficient=–0.44, –0.73 –0.15). Implementation restaurants bars not associated with (coefficient=0.004, –0.07 0.08). <title>CONCLUSIONS</title> started incidence announced. Considering time lags behavior changes, should be planned advance surge an sufficiently informed by data.