Abstract

During the COVID-19 pandemic, governments faced difficulties in implementing mobility restriction measures, as no clear quantitative relationship between human mobility and infection spread in large cities is known. We developed a model that enables quantitative estimations of the infection risk for individual places and activities by using smartphone GPS data for the Tokyo metropolitan area. The effective reproduction number is directly calculated from the number of infectious social contacts defined by the square of the population density at each location. The difference in the infection rate of daily activities is considered, where the ‘stay-out’ activity, staying at someplace neither home nor workplace, is more than 28 times larger than other activities. Also, the contribution to the infection strongly depends on location. We imply that the effective reproduction number is sufficiently suppressed if the highest-risk locations or activities are restricted. We also discuss the effects of the Delta variant and vaccination.

Details

Title
Direct modelling from GPS data reveals daily-activity-dependency of effective reproduction number in COVID-19 pandemic
Author
Ozaki, Jun’ichi 1 ; Shida, Yohei 2 ; Takayasu, Hideki 3 ; Takayasu, Misako 4 

 Tokyo Institute of Technology, Institute of Innovative Research, Yokohama, Japan (GRID:grid.32197.3e) (ISNI:0000 0001 2179 2105) 
 Tokyo Institute of Technology, Department of Mathematical and Computing Science, School of Computing, Yokohama, Japan (GRID:grid.32197.3e) (ISNI:0000 0001 2179 2105) 
 Tokyo Institute of Technology, Institute of Innovative Research, Yokohama, Japan (GRID:grid.32197.3e) (ISNI:0000 0001 2179 2105); Sony Computer Science Laboratories, Inc., Tokyo, Japan (GRID:grid.452725.3) (ISNI:0000 0004 1764 0071) 
 Tokyo Institute of Technology, Institute of Innovative Research, Yokohama, Japan (GRID:grid.32197.3e) (ISNI:0000 0001 2179 2105); Tokyo Institute of Technology, Department of Mathematical and Computing Science, School of Computing, Yokohama, Japan (GRID:grid.32197.3e) (ISNI:0000 0001 2179 2105) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2728335464
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.