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Open Access

Retraction

Retraction: Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting

  • The PLOS One Editors

The PLOS One Editors retract this article [1,2] 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 subsequently published in [3,4], raising concerns about redundant publication. We regret that the issues were not addressed prior to the article’s publication.

MUT, SBI, and MB did not agree with the retraction. AA either did not respond directly or could not be reached.

References

  1. 1. 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
  2. 2. 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
  3. 3. 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
  4. 4. The PLOS One Editors. Retraction: Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. PLoS ONE. 2025;20(4): e0321232.

Citation: The PLOS One Editors (2025) Retraction: Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. PLoS ONE 20(4): e0321233. https://doi.org/10.1371/journal.pone.0321233

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.