Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Correction

Correction: Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model

  • Mark J. Panaggio,
  • Mike Fang,
  • Hyunseung Bang,
  • Paige A. Armstrong,
  • Alison M. Binder,
  • Julian E. Grass,
  • Jake Magid,
  • Marc Papazian,
  • Carrie K. Shapiro-Mendoza,
  • Sharyn E. Parks

There are errors in the author affiliations. The correct affiliations are as follows:

Mark J. Panaggio1, Mike Fang1, Hyunseung Bang1, Paige A. Armstrong2, Alison M. Binder2, Julian E. Grass2, Jake Magid3, Marc Papazian3, Carrie K. Shapiro-Mendoza2, Sharyn E. Parks2

1 Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, USA, 2 COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, 3 Palantir Technologies, Denver, Colorado, USA.

Reference

  1. 1. Panaggio MJ, Fang M, Bang H, Armstrong PA, Binder AM, Grass JE, et al. (2023) Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model. PLoS ONE 18(10): e0292354. pmid:37792907

Citation: Panaggio MJ, Fang M, Bang H, Armstrong PA, Binder AM, Grass JE, et al. (2024) Correction: Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model. PLoS ONE 19(3): e0300861. https://doi.org/10.1371/journal.pone.0300861

Published: March 14, 2024

Copyright: © 2024 Panaggio et al. 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.