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Hotspot analysis of COVID-19 infection using mobile-phone location data

Restrictions on outdoor activities are required to suppress the COVID-19 pandemic. To monitor social risks and control the pandemic through sustainable restrictions, we focus on the relationship between the number of people going out and the effective reproduction number. The novelty of this study i...

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Autores principales: Kimura, Yu, Seki, Tatsunori, Miyata, Satoshi, Arai, Yusuke, Murata, Toshiki, Inoue, Hiroyasu, Ito, Nobuyasu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Japan 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9702898/
https://www.ncbi.nlm.nih.gov/pubmed/36467969
http://dx.doi.org/10.1007/s10015-022-00830-2
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author Kimura, Yu
Seki, Tatsunori
Miyata, Satoshi
Arai, Yusuke
Murata, Toshiki
Inoue, Hiroyasu
Ito, Nobuyasu
author_facet Kimura, Yu
Seki, Tatsunori
Miyata, Satoshi
Arai, Yusuke
Murata, Toshiki
Inoue, Hiroyasu
Ito, Nobuyasu
author_sort Kimura, Yu
collection PubMed
description Restrictions on outdoor activities are required to suppress the COVID-19 pandemic. To monitor social risks and control the pandemic through sustainable restrictions, we focus on the relationship between the number of people going out and the effective reproduction number. The novelty of this study is that we have considered influx population instead of staying-population, as the data represent congestion. This enables us to apply our analysis method to all meshes because the influx population may always represent the congestion of specific areas, which include the residential areas as well. In this study, we report the correlation between the influx population in downtown areas and business districts in Tokyo during the pandemic considering the effective reproduction number and associated time delay. Moreover, we validate our method and the influx population data by confirming the consistency of the results with those of the previous research and epidemiological studies. As a result, it is confirmed that the social risk with regard to the spread of COVID-19 infection when people travel to downtown areas and business districts is high, and the risk when people visit only residential areas is low.
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spelling pubmed-97028982022-11-28 Hotspot analysis of COVID-19 infection using mobile-phone location data Kimura, Yu Seki, Tatsunori Miyata, Satoshi Arai, Yusuke Murata, Toshiki Inoue, Hiroyasu Ito, Nobuyasu Artif Life Robot Original Article Restrictions on outdoor activities are required to suppress the COVID-19 pandemic. To monitor social risks and control the pandemic through sustainable restrictions, we focus on the relationship between the number of people going out and the effective reproduction number. The novelty of this study is that we have considered influx population instead of staying-population, as the data represent congestion. This enables us to apply our analysis method to all meshes because the influx population may always represent the congestion of specific areas, which include the residential areas as well. In this study, we report the correlation between the influx population in downtown areas and business districts in Tokyo during the pandemic considering the effective reproduction number and associated time delay. Moreover, we validate our method and the influx population data by confirming the consistency of the results with those of the previous research and epidemiological studies. As a result, it is confirmed that the social risk with regard to the spread of COVID-19 infection when people travel to downtown areas and business districts is high, and the risk when people visit only residential areas is low. Springer Japan 2022-11-27 2023 /pmc/articles/PMC9702898/ /pubmed/36467969 http://dx.doi.org/10.1007/s10015-022-00830-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Kimura, Yu
Seki, Tatsunori
Miyata, Satoshi
Arai, Yusuke
Murata, Toshiki
Inoue, Hiroyasu
Ito, Nobuyasu
Hotspot analysis of COVID-19 infection using mobile-phone location data
title Hotspot analysis of COVID-19 infection using mobile-phone location data
title_full Hotspot analysis of COVID-19 infection using mobile-phone location data
title_fullStr Hotspot analysis of COVID-19 infection using mobile-phone location data
title_full_unstemmed Hotspot analysis of COVID-19 infection using mobile-phone location data
title_short Hotspot analysis of COVID-19 infection using mobile-phone location data
title_sort hotspot analysis of covid-19 infection using mobile-phone location data
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9702898/
https://www.ncbi.nlm.nih.gov/pubmed/36467969
http://dx.doi.org/10.1007/s10015-022-00830-2
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