Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. While wastewater monitoring has been implemented to mitigate outbreak risk in universities and residential settings, its effectiveness in community K-12 sites is unknown. We implemented a wastewater and surface monitoring system to detect SARS-CoV-2 in nine elementary schools in San Diego County. Ninety-three percent of identified cases were associated with either a positive wastewater or surface sample; 67% were associated with a positive wastewater sample, and 40% were associated with a positive surface sample. The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Passive environmental surveillance can complement approaches that require individual consent, particularly in communities with limited access and/or high rates of testing hesitancy.

Brain-inspired Hyperdimensional (HD) computing is a promising solution for energy-efficient classification. HD emulates cognition tasks by exploiting long-size vectors instead of working with numeric values used in contemporary processors. However, the existing HD computing algorithms have lack of controllability on the training iterations which often results in slow training or divergence. In this work, we propose AdaptHD, an adaptive learning approach based on HD computing to address the HD training issues. AdaptHD introduces the definition of learning rate in HD computing and proposes two approaches for adaptive training: iteration-dependent and data-dependent. In the iteration-dependent approach, AdaptHD uses a large learning rate to speedup the training procedure in the first iterations, and then adaptively reduces the learning rate depending on the slope of the error rate. In the data-dependent approach, AdaptHD changes the learning rate for each data point depending on how far off the data was misclassified. Our evaluations on a wide range of classification applications show that AdaptHD achieves 6.9× speedup and 6.3× energy efficiency improvement during training as compared to the state-of-the-art HD computing algorithm.

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