TY - JOUR
T1 - A systematic literature review of time series methods applied to epidemic prediction
AU - Batoure Bamana, Apollinaire
AU - Shafiee Kamalabad, Mahdi
AU - Oberski, Daniel L.
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - African context
KW - COVID-19 impact
KW - Epidemic prediction
KW - Missing data
KW - Outlier data
KW - Time series forecasting
KW - Time series methods
UR - http://www.scopus.com/inward/record.url?scp=85201310881&partnerID=8YFLogxK
U2 - 10.1016/j.imu.2024.101571
DO - 10.1016/j.imu.2024.101571
M3 - Review article
AN - SCOPUS:85201310881
VL - 50
JO - Informatics in Medicine Unlocked
JF - Informatics in Medicine Unlocked
M1 - 101571
ER -