A systematic literature review of time series methods applied to epidemic prediction

Apollinaire Batoure Bamana*, Mahdi Shafiee Kamalabad, Daniel L. Oberski

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

While time series are extensively utilized in economics, finance and meteorology, their application in epidemics has been comparatively limited. To facilitate a comprehensive research endeavor on this matter, we deemed it necessary to commence with a systematic literature review (SLR). This Systematic Literature Review aims to assess, based on a sample of relevant papers, the use of Time Series Methods (TSM) in epidemic prediction, with a special focus on African issues and the impact of COVID-19. The SLR was conducted using databases such as ACM, IEEE, PubMed and Science Direct. Open access published papers in English, in a pear reviewed Journals, from 2014 to 2023, containing keywords such as Time Series, Epidemic and Prediction were selected. The findings were summarized in an adapted PRISMA flow diagram. We end up with a sample of 36 papers. As conclusion, TSM are not so used in epidemic prediction as in some other domains, even though epidemic data are collected as time series. Just very few works address African issues regarding diseases and countries. COVID-19 is the pandemic that revealed and enhanced the used of TSM to forecast epidemics. This work paves ways for R&D on epidemiology, based on TSM.

Original languageEnglish
Article number101571
JournalInformatics in Medicine Unlocked
Volume50
DOIs
Publication statusPublished - 2024

Keywords

  • African context
  • COVID-19 impact
  • Epidemic prediction
  • Missing data
  • Outlier data
  • Time series forecasting
  • Time series methods

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