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In this study, learning modalities offered by public schools across theUnited States were investigated to track changes in the proportion of schoolsoffering fully in-person, hybrid and fully remote learning over time. Learningmodalities from 14,688 unique school districts from September 2020 to June 2021were reported by Burbio, MCH Strategic Data, the American EnterpriseInstitute's Return to Learn Tracker and individual state dashboards. A modelwas needed to combine and deconflict these data to provide a more completedescription of modalities nationwide.  A hidden Markov model (HMM) was used to infer the most likely learningmodality for each district on a weekly basis. This method yielded higherspatiotemporal coverage than any individual data source and higher agreementwith three of the four data sources than any other single source. The modeloutput revealed that the percentage of districts offering fully in-personlearning rose from 40.3% in September 2020 to 54.7% in June of 2021 withincreases across 45 states and in both urban and rural districts. This type ofprobabilistic model can serve as a tool for fusion of incomplete andcontradictory data sources in support of public health surveillance andresearch efforts.

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