{"links":{},"id":214669,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214669","sets":["6504:10735:10805"]},"path":["10805"],"owner":"44499","recid":"214669","title":["人流ビッグデータを用いた新型コロナ感染予測と要因推定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"0c2e90e1-6e33-453a-8498-c2ae731900c8"},"_deposit":{"id":"214669","pid":{"type":"depid","value":"214669","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"人流ビッグデータを用いた新型コロナ感染予測と要因推定","author_link":["551868","551870","551867","551869"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"人流ビッグデータを用いた新型コロナ感染予測と要因推定"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ソフトウェア科学・工学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2021-03-04","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"国立情報学研究所・株式会社KDDI総合研究所"},{"subitem_text_value":"KDDI総合研究所"},{"subitem_text_value":"KDDI総合研究所"},{"subitem_text_value":"株式会社KDDI総合研究所・KDDI株式会社"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/214669/files/IPSJ-Z83-2B-04.pdf","label":"IPSJ-Z83-2B-04.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-2B-04.pdf","filesize":[{"value":"309.3 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"da185dd3-40f9-4f2c-b52d-cf5d6b4b3cf6","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"水野, 貴之"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"上坂, 大輔"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"幡, 容子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"南川, 敦宣"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"飲食店が多いなどの地域の属性情報と,携帯電話から補足した移動履歴の人流情報を用いて,機械学習により各地の新型コロナウイルス感染者数を予測するモデルを構築する.これにより,感染者数を予測する上で,重要な地理的な要因は何であるかを明らかにする.感染者を予測する上で重要な要因は,地域の人口,人口あたりの外国人比率,人流が滞留するコミュニティであることが明らかになった.コミュニティを跨ぐ移動を抑制するゾーニングをおこなうことにより,ウィルスの封じ込めと経済活動とを両立できることを示す.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"120","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"119","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:15:28.433557+00:00","updated":"2025-01-19T16:31:24.383441+00:00"}