COVID-19-Related School Closures and Learning Modality Changes - United States, August 1-September 17, 2021.

Sharyn E Parks,Amra Uzicanin,Lisa C Barrios,Emma Raible,Mark J Panaggio,Samantha E Budzyn,Jake Magid,Faruque Ahmed,Nicole Zviedrite,Marc Papazian

doi:10.15585/mmwr.mm7039e2

Abstract

Beginning in January 2021, the U.S. government prioritized ensuring continuity of learning for all students during the COVID-19 pandemic (1). To estimate the extent of COVID-19-associated school disruptions, CDC and the Johns Hopkins University Applied Physics Laboratory used a Hidden Markov Model (HMM) (2) statistical approach to estimate the most likely actual learning modality based on patterns observed in past data, accounting for conflicting or missing information and systematic Internet searches (3) for COVID-19-related school closures. This information was used to assess how many U.S. schools were open, and in which learning modalities, during August 1-September 17, 2021. Learning modalities included 1) full in-person learning, 2) a hybrid of in-person and remote learning, and 3) full remote learning.

Highlights

Multiple data sources were combined to estimate the learning modality for public and public charter school districts in the United States using HMM; sources included Burbio,* MCH Strategic Data,† American Enterprise Institute–Return to Learn,§ and state dashboards.¶ Weekly learning modalities during August 1, 2020– July 31, 2021 were used to select the optimal weights for each reported modality in order to infer the most likely actual learning modality
8,343 (96%) were offering full in-person learning, 322 (4%) were offering hybrid learning, and 35 (0.4%) were offering full remote learning
The timing of return to school likely accounts for some regional variation in school closures because longer in-session time increases opportunities for COVID-19 cases to appear in schools

Summary

Introduction

Multiple data sources were combined to estimate the learning modality for public and public charter school districts in the United States using HMM; sources included Burbio,* MCH Strategic Data,† American Enterprise Institute–Return to Learn,§ and state dashboards.¶ Weekly learning modalities (full in-person, hybrid, and full remote) during August 1, 2020– July 31, 2021 were used to select the optimal weights for each reported modality in order to infer the most likely actual learning modality. * https://cai.burbio.com/school-opening-tracker/ † https://www.mchdata.com/covid19/schoolclosings § https://www.returntolearntracker.net/ ¶ Colorado, Connecticut, Hawaii, Idaho, Illinois, Louisiana, Minnesota, Missouri, New Mexico, North Carolina, Ohio, Oregon, South Carolina, Tennessee, Vermont, Virginia, and Washington. Closure dates and reasons were recorded and linked to publicly available education data.** HMM was fitted using the Pomegranate module (version 0.14.3) for Python (version 3.7.6). For the week ending September 17, 2021, HMM data were available for 73% of kindergarten through grade 12 public school students in 8,700 districts nationwide and varied by state (Supplementary Figure, https://stacks.cdc.gov/view/ cdc/109969).

Results

Conclusion