Hotspot analysis of COVID-19 infection in Tokyo based on influx patterns
Abstract We analyse the relationship between population influx and the effective reproduction number in the 23 wards of Tokyo during the COVID-19 pandemic to estimate hotspots of infection. We identify some patterns of population influx via factor analysis and estimate specific areas as infection-re...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-82962-y |
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| _version_ | 1841544585093840896 |
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| author | Yu Kimura Tatsunori Seki Keisuke Chujo Toshiki Murata Tomoaki Sakurai Satoshi Miyata Hiroyasu Inoue Nobuyasu Ito |
| author_facet | Yu Kimura Tatsunori Seki Keisuke Chujo Toshiki Murata Tomoaki Sakurai Satoshi Miyata Hiroyasu Inoue Nobuyasu Ito |
| author_sort | Yu Kimura |
| collection | DOAJ |
| description | Abstract We analyse the relationship between population influx and the effective reproduction number in the 23 wards of Tokyo during the COVID-19 pandemic to estimate hotspots of infection. We identify some patterns of population influx via factor analysis and estimate specific areas as infection-related hotspots by focusing on influx patterns that are highly correlated with the effective reproduction number. As a result, several influx patterns are assumed to be directly related to the subsequent spread of the infection. This analytical method has the potential to detect unknown hotspots related to pandemics in the future. |
| format | Article |
| id | doaj-art-5809ebfafe08403f9642384f9fa53f1a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5809ebfafe08403f9642384f9fa53f1a2025-01-12T12:24:31ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-024-82962-yHotspot analysis of COVID-19 infection in Tokyo based on influx patternsYu Kimura0Tatsunori Seki1Keisuke Chujo2Toshiki Murata3Tomoaki Sakurai4Satoshi Miyata5Hiroyasu Inoue6Nobuyasu Ito7SoftBank CorporationSoftBank CorporationSoftBank CorporationSoftBank CorporationSoftBank CorporationSoftBank CorporationGraduate School of Information Science, University of HyogoRIKEN Center for Computational ScienceAbstract We analyse the relationship between population influx and the effective reproduction number in the 23 wards of Tokyo during the COVID-19 pandemic to estimate hotspots of infection. We identify some patterns of population influx via factor analysis and estimate specific areas as infection-related hotspots by focusing on influx patterns that are highly correlated with the effective reproduction number. As a result, several influx patterns are assumed to be directly related to the subsequent spread of the infection. This analytical method has the potential to detect unknown hotspots related to pandemics in the future.https://doi.org/10.1038/s41598-024-82962-y |
| spellingShingle | Yu Kimura Tatsunori Seki Keisuke Chujo Toshiki Murata Tomoaki Sakurai Satoshi Miyata Hiroyasu Inoue Nobuyasu Ito Hotspot analysis of COVID-19 infection in Tokyo based on influx patterns Scientific Reports |
| title | Hotspot analysis of COVID-19 infection in Tokyo based on influx patterns |
| title_full | Hotspot analysis of COVID-19 infection in Tokyo based on influx patterns |
| title_fullStr | Hotspot analysis of COVID-19 infection in Tokyo based on influx patterns |
| title_full_unstemmed | Hotspot analysis of COVID-19 infection in Tokyo based on influx patterns |
| title_short | Hotspot analysis of COVID-19 infection in Tokyo based on influx patterns |
| title_sort | hotspot analysis of covid 19 infection in tokyo based on influx patterns |
| url | https://doi.org/10.1038/s41598-024-82962-y |
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