Urban Classification, Not COVID-19 Community Rates, Was Associated With Modes of Learning in US K-12 Schools?

OBJECTIVES: To identify factors associated with the decision to provide in-person, hybrid, and remote learning in kindergarten through 12th grade school districts during the 2020-2021 school year. METHODS: We performed a retrospective study evaluating school district mode of learning and community coronavirus 2019 (COVID-19) incidence and percentage positivity rates at 3 time points during the pandemic: (1) September 15, 2020 (the beginning of the school year, before Centers for Disease Control and Prevention guidance); (2) November 15, 2020 (midsemester after the release of Centers for Disease Control and Prevention guidance and an increase of COVID-19 cases); and (3) January 15, 2021 (start of the second semester and peak COVID-19 rates). Five states were included in the analysis: Michigan, Missouri, North Carolina, Ohio, and Wisconsin. The primary outcome was mode of learning in elementary, middle, and high schools during 3 time points. The measures included community COVID-19 incidence and percentage positivity rates, school and student demographics, and county size classification of school location. RESULTS: No relationship between mode of learning and community COVID-19 rates was observed. County urban classification of school location was associated with mode of learning with school districts in nonmetropolitan and small metropolitan counties more likely to be in-person. CONCLUSIONS: Community COVID-19 rates did not appear to influence the decision of when to provide in-person learning. Further understanding of factors driving the decisions to bring children back into the classroom are needed. Standardizing policies on how schools apply national guidance to local decision-making may decrease disparities in emergent crises.

Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm.

The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.