Operating system

Political science

Disease

Coronavirus disease 2019 (COVID-19)

Biology

Computer science

2019-20 coronavirus outbreak

Computational Science and Engineering

Scopus

Machine learning

Engineering

Section (typography)

Medicine

Mechanical engineering

Library science

MEDLINE

Work (physics)

Infectious disease (medical specialty)

Outbreak

Artificial intelligence

Subject (documents)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Virology

Data science

Web of science

Pathology

Neural Computing and Applications

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.

A review on COVID-19 forecasting models