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http://hdl.handle.net/10397/94332
Title: | Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis | Authors: | Yamanishi, K Xu, L Yuki, R Fukushima, S Lin, CH |
Issue Date: | 2021 | Source: | Scientific reports, 2021, v. 11, 19795 | Abstract: | We are concerned with the issue of detecting changes and their signs from a data stream. For example, when given time series of COVID-19 cases in a region, we may raise early warning signals of an epidemic by detecting signs of changes in the data. We propose a novel methodology to address this issue. The key idea is to employ a new information-theoretic notion, which we call the differential minimum description length change statistics (D-MDL), for measuring the scores of change sign. We first give a fundamental theory for D-MDL. We then demonstrate its effectiveness using synthetic datasets. We apply it to detecting early warning signals of the COVID-19 epidemic using time series of the cases for individual countries. We empirically demonstrate that D-MDL is able to raise early warning signals of events such as significant increase/decrease of cases. Remarkably, for about 64 % of the events of significant increase of cases in studied countries, our method can detect warning signals as early as nearly six days on average before the events, buying considerably long time for making responses. We further relate the warning signals to the dynamics of the basic reproduction number R0 and the timing of social distancing. The results show that our method is a promising approach to the epidemic analysis from a data science viewpoint. | Publisher: | Nature Publishing Group | Journal: | Scientific reports | EISSN: | 2045-2322 | DOI: | 10.1038/s41598-021-98781-4 | Rights: | This 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/. © The Author(s) 2021 The following publication Yamanishi, K., Xu, L., Yuki, R., Fukushima, S., & Lin, C. H. (2021). Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis. Scientific reports, 11, 19795 is available at https://doi.org/10.1038/s41598-021-98781-4 |
Appears in Collections: | Journal/Magazine Article |
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