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CORRECTION article

Front. Public Health, 22 February 2024
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 12 - 2024 | https://doi.org/10.3389/fpubh.2024.1381284

Corrigendum: Detection of COVID-19 epidemic outbreak using machine learning

This article is a correction to:

  1. Detection of COVID-19 epidemic outbreak using machine learning

    1. Read original article
\r\nGiphil Cho&#x;Giphil Cho1†Jeong Rye Park&#x;Jeong Rye Park2†Yongin ChoiYongin Choi3Hyeonjeong AhnHyeonjeong Ahn4Hyojung Lee Hyojung Lee4*
  • 1Department of Artificial Intelligence and Software, Kangwon National University, Samcheok-si, Republic of Korea
  • 2Department of Mathematics, Kyungpook National University, Daegu, Republic of Korea
  • 3Busan Center for Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea
  • 4Department of Statistics, Kyungpook National University, Daegu, Republic of Korea

A corrigendum on
Detection of COVID-19 epidemic outbreak using machine learning

by Cho, G., Park, J. R., Choi, Y., Ahn, H., and Lee, H. (2023). Front. Public Health. 11:1252357. doi: 10.3389/fpubh.2023.1252357

In the published article, there was an error in the Funding statement. The correct Funding statement appears below.

Funding

HL was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2022R1C1C1006237, NRF-2022R1A5A1033624, RS-2023-00227944). GC was supported by an NRF grant funded by the Korean government (No. NRF-2020R1C1C1A01012557). JP was supported by an NRF grant funded by the Korean government (No. NRF- 2021R1I1A1A01057767). YC was supported by a National Institute for Mathematical Sciences (NIMS) grant funded by the Korean government (MSIT) (No. B23820000).

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: COVID-19, prediction, machine learning, early detection, outbreak

Citation: Cho G, Park JR, Choi Y, Ahn H and Lee H (2024) Corrigendum: Detection of COVID-19 epidemic outbreak using machine learning. Front. Public Health 12:1381284. doi: 10.3389/fpubh.2024.1381284

Received: 03 February 2024; Accepted: 05 February 2024;
Published: 22 February 2024.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2024 Cho, Park, Choi, Ahn and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Hyojung Lee, hjlee@knu.ac.kr

†These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.