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This analysis will use surveillance data on laboratory-confirmed influenza and influenza hospitalization as well as school absenteeism to evaluate the impact of the Shoo the Flu program.

This analysis evaluated the impact of the Shoo the Flu program on school aged vaccination coverage and community hospitalizations with respect to race and ethnicity.

UC Berkeley is collaborating with Kaiser to analyze Kaiser patient data as part of the evaluation of Shoo the Flu.

Open procedures represent the dominant form of surgery worldwide. Artificial intelligence (AI) has the potential to optimize surgical practice and improve patient outcomes, but efforts have focused primarily on minimally invasive techniques. Our work overcomes existing data limitations for training AI models by curating, from YouTube, the largest dataset of open surgical videos to date: 1997 videos from 23 surgical procedures uploaded from 50 countries. Using this dataset, we developed a multi-task AI model capable of real-time understanding of surgical behaviors, hands, and tools - the building blocks of procedural flow and surgeon skill. We show that our model generalizes across diverse surgery types and environments. Illustrating this generalizability, we directly applied our YouTube-trained model to analyze open surgeries prospectively collected at an academic medical center and identified kinematic descriptors of surgical skill related to efficiency of hand motion. Our Annotated Videos of Open Surgery (AVOS) dataset and trained model will be made available for further development of surgical AI., Comment: 22 pages, 4 main text figures, 7 extended data figures, 4 extended data tables

The U.S. Food & Drug Administration (FDA) requires that e-cigarette advertisements include a prominent warning label that reminds consumers that nicotine is addictive. However, the high volume of vaping-related posts on social media makes compliance auditing expensive and time-consuming, suggesting that an automated, scalable method is needed. We sought to develop and evaluate a deep learning system designed to automatically determine if an Instagram post promotes vaping, and if so, if an FDA-compliant warning label was included or if a non-compliant warning label was visible in the image. We compiled and labeled a dataset of 4,363 Instagram images, of which 44% were vaping-related, 3% contained FDA-compliant warning labels, and 4% contained non-compliant labels. Using a 20% test set for evaluation, we tested multiple neural network variations: image processing backbone model (Inceptionv3, ResNet50, EfficientNet), data augmentation, progressive layer unfreezing, output bias initialization designed for class imbalance, and multitask learning. Our final model achieved an area under the curve (AUC) and [accuracy] of 0.97 [92%] on vaping classification, 0.99 [99%] on FDA-compliant warning labels, and 0.94 [97%] on non-compliant warning labels. We conclude that deep learning models can effectively identify vaping posts on Instagram and track compliance with FDA warning label requirements., 9 pages, 3 figures

This paper examines how public libraries can best use social media as a marketing tool, specifically focusing on Facebook. Data was collected from 30 public library Facebook pages on number of posts, number of comments, and number of likes received during a two month period. This data was used to create a model that could fairly accurately predict how many likes a public library's Facebook page would receive based on several factors. Findings of this study suggest that a public library can effectively cultivate public interest through social media by posting to social media sites frequently, allowing for comments on social media sites, and posting about career achievements of current staff members.