Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis

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...

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Autores principales:	Kenji Yamanishi, Linchuan Xu, Ryo Yuki, Shintaro Fukushima, Chuan-hao Lin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e2cedf0a469d4e2fa76e74ffc0c17ddd
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spelling	oai:doaj.org-article:e2cedf0a469d4e2fa76e74ffc0c17ddd2021-12-02T19:16:18ZChange sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis10.1038/s41598-021-98781-42045-2322https://doaj.org/article/e2cedf0a469d4e2fa76e74ffc0c17ddd2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98781-4https://doaj.org/toc/2045-2322Abstract 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 $$64\%$$ 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.Kenji YamanishiLinchuan XuRyo YukiShintaro FukushimaChuan-hao LinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kenji Yamanishi Linchuan Xu Ryo Yuki Shintaro Fukushima Chuan-hao Lin Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis
description	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 $$64\%$$ 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.
format	article
author	Kenji Yamanishi Linchuan Xu Ryo Yuki Shintaro Fukushima Chuan-hao Lin
author_facet	Kenji Yamanishi Linchuan Xu Ryo Yuki Shintaro Fukushima Chuan-hao Lin
author_sort	Kenji Yamanishi
title	Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis
title_short	Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis
title_full	Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis
title_fullStr	Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis
title_full_unstemmed	Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis
title_sort	change sign detection with differential mdl change statistics and its applications to covid-19 pandemic analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e2cedf0a469d4e2fa76e74ffc0c17ddd
work_keys_str_mv	AT kenjiyamanishi changesigndetectionwithdifferentialmdlchangestatisticsanditsapplicationstocovid19pandemicanalysis AT linchuanxu changesigndetectionwithdifferentialmdlchangestatisticsanditsapplicationstocovid19pandemicanalysis AT ryoyuki changesigndetectionwithdifferentialmdlchangestatisticsanditsapplicationstocovid19pandemicanalysis AT shintarofukushima changesigndetectionwithdifferentialmdlchangestatisticsanditsapplicationstocovid19pandemicanalysis AT chuanhaolin changesigndetectionwithdifferentialmdlchangestatisticsanditsapplicationstocovid19pandemicanalysis
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Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis

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