The PLOS One Editors retract this article due to concerns about peer review integrity, authorship, and adherence to the journal’s publication requirements on reporting and data availability. Among other concerns, most of Table 1 and Figs 2–10 were published previously in [2–4], which was not cited or discussed in [1]. We regret that the issues were not addressed prior to the article’s publication.
All authors did not agree with the retraction.
References
- 1.. Tariq MU, Ismail SB. Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. PLoS ONE. 2024;19(3): e0294289. pmid:38483948
- 2.. Tariq MU, Ismail SB, Babar M, Ahmad A. Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. PLoS ONE. 2023;18(7):e0287755. pmid:37471397
- 3.. Tariq MU, Ismail SB, Babar M, Ahmad A. Correction: Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. PLoS ONE. 2023;18(12): e0296111. pmid:38096185
- 4.. The PLOS One Editors. Retraction: Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. PLoS ONE. 2025;20(4): e0321233.
Citation: The PLOS One Editors (2025) Retraction: Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. PLoS ONE 20(4): e0321232. https://doi.org/10.1371/journal.pone.0321232
Published: April 9, 2025
Copyright: © 2025 The PLOS One Editors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.