Real-time Forecasting of Co-evolving Epidemics

• Kimura, Tasuku

• Matsubara, Yasuko

• Kawabata, Koki

• Sakurai, Yasushi

Bibliographic Information

Other Title

• 大規模疫病データのための将来予測アルゴリズム

Abstract

Given a large collection of co-evolving epidemics, how can we forecast their future characteristics? In this paper, we propose a streaming algorithm, EpiCast, which is able to model, understand and forecast future epidemic outbreaks as well as pandemics. Our method has the following features for the effective and efficient modeling of the dynamics of spreading viruses. (a) Non-linear: we incorporate a non-linear equation that is suitable for complex epidemic modeling. (b) Dynamic: it maintains multiple such non-linear models to share important patterns among locations, and chooses the non-linear model for the forecast while monitoring a co-evolving epidemic data stream. (c) Scalable: it can quickly forecast future phenomena at any time in a practically constant time. In extensive experiments using real COVID-19 datasets over major countries, we demonstrate that our proposed method outperforms existing methods for time series in terms of forecasting accuracy, and significantly reduces the required computational time.

Journal

• 情報処理学会論文誌データベース（TOD）

  情報処理学会論文誌データベース（TOD） 14 (2), 10-19, 2021-04-13

  情報処理学会

Keywords

• epidemics
• time series
• non-linear dynamical systems
• real-time forecasting

Details

• CRID: 1050018218946106752
• NII Article ID: 170000184858
• NII Book ID: AA11464847
• ISSN: 18827799
• HANDLE: 11094/93124
• Web Site: http://id.nii.ac.jp/1001/00210534/
• Text Lang: ja
• Article Type: journal article
• Data Source:
  • IRDB
  • CiNii Articles
  • KAKEN