A review on COVID-19 forecasting models

Iman Rahimi; Fang Chen; Amir H Gandomi

doi:10.1007/s00521-020-05626-8

A review on COVID-19 forecasting models

Neural Comput Appl. 2021 Feb 4:1-11. doi: 10.1007/s00521-020-05626-8. Online ahead of print.

Authors

Iman Rahimi¹, Fang Chen², Amir H Gandomi²

Affiliations

¹ Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Seri Kembangan, Malaysia.
² Data Science Institute, University of Technology Sydney, Ultimo, 2007 NSW Australia.

Abstract

The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.

Keywords: Analysis; COVID-19; Forecasting; SEIR; SIR; Time series.

Publication types

Review