Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8503, Japan.
Department of Mathematical and Computing Science, School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8503, Japan.
Sci Rep. 2022 Oct 25;12(1):17888. doi: 10.1038/s41598-022-22420-9.
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.
在 COVID-19 大流行期间，各国政府在实施流动限制措施方面遇到困难，因为目前尚不清楚大城市中人类流动性与感染传播之间存在何种明确的定量关系。我们开发了一种模型，该模型使用东京大都市区的智能手机 GPS 数据，可以对各个地点和活动的感染风险进行定量估计。有效繁殖数是通过在每个位置的人口密度的平方定义的传染性社会接触数量直接计算得出的。还考虑了日常活动感染率的差异，其中“外出”活动（既不在家也不在工作场所的逗留）比其他活动大 28 多倍。此外，感染的贡献强烈取决于位置。如果限制高风险地点或活动，我们暗示有效繁殖数会受到充分抑制。我们还讨论了 Delta 变体和疫苗接种的影响。
