JOURNAL ARTICLE

Forecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-19.

  • Published In: Mathematical Medicine & Biology: A Journal of the IMA, 2025, v. 42, n. 1. P. 98 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Demongeot, Jacques; Magal, Pierre; Oshinubi, Kayode 3 of 3

Abstract

The article focuses on developing a forecasting method to predict the transition from endemic to epidemic phases in contagious diseases, specifically applied to COVID-19 data from France, India, and Japan. It uses four statistical indicators—coefficient of variation, skewness, kurtosis, and entropy—calculated from the empirical distribution of daily new reported cases over a 14-day moving window to capture changes in the distribution's shape. Principal component analysis (PCA) combines these indicators into a single score that explains a significant portion of the variance and serves as a predictor for the onset of epidemic waves. The method shows moderate predictive accuracy across the three countries studied and is proposed as a potential early warning tool, with further validation needed on other diseases and datasets.

Additional Information

  • Source:Mathematical Medicine & Biology: A Journal of the IMA. 2025/03, Vol. 42, Issue 1, p98
  • Document Type:Article
  • Subject Area:Life Sciences
  • Publication Date:2025
  • ISSN:1477-8599
  • DOI:10.1093/imammb/dqae012
  • Accession Number:184297498
  • Copyright Statement:Copyright of Mathematical Medicine & Biology: A Journal of the IMA is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.