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Robust two-stage influenza prediction model considering regular and irregular trends

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dc.contributor.author Murayama, Taichi en
dc.contributor.author Shimizu, Nobuyuki en
dc.contributor.author Wakamiya, Shoko en
dc.contributor.author Aramaki, Eiji en
dc.date.accessioned 2020-11-25T09:11:47Z en
dc.date.available 2020-11-25T09:11:47Z en
dc.date.issued 2020-05-21 en
dc.identifier.uri http://hdl.handle.net/10061/14156 en
dc.description.abstract Influenza causes numerous deaths worldwide every year. Predicting the number of influenza patients is an important task for medical institutions. Two types of data regarding influenza-like illnesses (ILIs) are often used for flu prediction: (1) historical data and (2) user generated content (UGC) data on the web such as search queries and tweets. Historical data have an advantage against the normal state but show disadvantages against irregular phenomena. In contrast, UGC data are advantageous for irregular phenomena. So far, no effective model providing the benefits of both types of data has been devised. This study proposes a novel model, designated the two-stage model, which combines both historical and UGC data. The basic idea is, first, basic regular trends are estimated using the historical data-based model, and then, irregular trends are predicted by the UGC data-based model. Our approach is practically useful because we can train models separately. Thus, if a UGC provider changes the service, our model could produce better performance because the first part of the model is still stable. Experiments on the US and Japan datasets demonstrated the basic feasibility of the proposed approach. In the dropout (pseudo-noise) test that assumes a UGC service would change, the proposed method also showed robustness against outliers. The proposed model is suitable for prediction of seasonal flu. en
dc.language.iso en en
dc.publisher Public Library of Science en
dc.relation.isreplacedby https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233126 en
dc.rights © 2020 Murayama et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ja
dc.title Robust two-stage influenza prediction model considering regular and irregular trends en
dc.type.nii Journal Article en
dc.contributor.alternative 村山, 太一 ja
dc.contributor.alternative 若宮, 翔子 ja
dc.contributor.alternative 荒牧, 英治 ja
dc.textversion none en
dc.identifier.eissn 1932-6203 en
dc.identifier.jtitle PLoS ONE en
dc.relation.doi 10.1371/journal.pone.0233126 en
dc.identifier.NAIST-ID 85627180 en
dc.identifier.NAIST-ID 74652314 en
dc.identifier.NAIST-ID 74652181 en


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