{"updated":"2025-01-21T21:11:33.441584+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00075530","sets":["934:989:6318:6491"]},"path":["6491"],"owner":"11","recid":"75530","title":["感染症拡大予測モデルとその考察"],"pubdate":{"attribute_name":"公開日","attribute_value":"2011-07-20"},"_buckets":{"deposit":"7a4db66d-f88a-453e-bc19-694b307d2841"},"_deposit":{"id":"75530","pid":{"type":"depid","value":"75530","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"感染症拡大予測モデルとその考察","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"感染症拡大予測モデルとその考察"},{"subitem_title":"Infectious Disease Spread Prediction Models and Consideration","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"オリジナル論文","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2011-07-20","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"九州工業大学"},{"subitem_text_value":"九州工業大学"},{"subitem_text_value":"九州工業大学"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Kyushu Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Kyushu Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Kyushu Institute of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/75530/files/IPSJ-TOM0403010.pdf"},"date":[{"dateType":"Available","dateValue":"2013-07-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM0403010.pdf","filesize":[{"value":"727.6 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"e5064bc5-e107-47fd-a7e3-1adb09eaede1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2011 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"廣瀬, 英雄"},{"creatorName":"松隈, 和広"},{"creatorName":"作村, 建紀"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hideo, Hirose","creatorNameLang":"en"},{"creatorName":"Kazuhiro, Matsuguma","creatorNameLang":"en"},{"creatorName":"Tatenori, Sakumura","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"感染症拡大を予測するパンデミックシミュレーションはシナリオによるシミュレーションとして取り扱われてきたが，実際にパンデミックが起こり始めると，観測データを使いながら将来どのようになるかを予測できるかということが重要になってくる．モデルの構造を仮定し，観測データを利用してモデルのパラメータを同定しながら予測を進める方法論は，データ同化とかグレーボックスとも呼ばれているが，パンデミック予測を行ううえでもこのことが必要になってくる．ここでは，微分方程式によるSIRモデルのパラメータを観測データから精確に推定するBBS法を提案し，またこれまで実際に観測された，SARS，口蹄疫のデータを用いて予測を行った結果について議論する．また，これをtruncatedモデルによる予測結果とも比較する．比較の結果，SIRモデルは最悪のケースを早期に予測する可能性があるが，truncatedモデルはかなり無力であることが分かった．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"For infectious disease spread prediction models, pandemic simulations have been dealt with as a kind of simulation by scenario. However, when a pandemic occurs, predicting the future using the observed data becomes crucial. The methodology that assumes the model structure and estimates the model parameters using the observed data, which is called the assimilation or the gray box, is also necessary in the pandemic analysis. In this paper, we propose a method to estimate such parameters, called the BBS (best-backward solution) method, and discuss the prediction results for observed real cases such as the SARS and the FMD (foot-and-mouth disease). We compare the results with those using the truncated model. We have found that the SIR model provides the worst case predictions even in early stages of pandemics contrary to the truncated model.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"109","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"102","bibliographicIssueDates":{"bibliographicIssueDate":"2011-07-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"4"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-18T23:32:36.701077+00:00","id":75530,"links":{}}