The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected communities of color. To dismantle these disparities, it is critical to promote COVID-19 vaccine equity, both through increasing vaccine access and addressing vaccine mistrust. This article describes a community–academic collaboration (the Community Vaccine Collaborative [CVC]), whose mission is to ensure COVID-19 vaccine equity among marginalized communities. Based in Pittsburgh, Pennsylvania, our group has focused on inclusion of marginalized groups into vaccine clinical trials, addressing vaccine mistrust, and building systems to ensuring equitable access to the COVID-19 vaccine. We review formation of the CVC, activities to-date, and recommendations for other communities interested in developing similar collaboratives.

he coronavirus disease 2019 (COVID-19) pandemic disproportionately impacts Black and Latine communities, who are experiencing health and economic ramifications of the pandemic at higher rates compared with non-Hispanic white communities. [1][2][3][4][5][6][7] Ensuring equitable access to the COVID-19 vaccine is critical to reducing these disparities and improving health. Mistrust of medical professionals, research institutions, and governmental agencies

Although Binary Relevance (BR) is an adaptive and conceptually simple multi-label learning technique, its inability to exploit label dependencies and other inherent problems in multi-label examples makes it difficult to generalize well in the classification of real-world multi-label examples like annotated images. Thus, to strengthen the generalization ability of Binary Relevance, this study used Multi-label Linear Discriminant Analysis (MLDA) as a preprocessing technique to take care of the label dependencies, the curse of dimensionality, and label over counting inherent in multi-labeled images. After that, Binary Relevance with K Nearest Neighbor as the base learner was fitted and its classification performance was evaluated on randomly selected 1000 images with a label cardinality of 2.149 of the five most frequent categories, namely; "person", "chair", "bottle", "dining table" and "cup" in the Microsoft Common Objects in Context 2017 (MS COCO 2017) dataset. Experimental results showed that micro averages of precision, recall, and f1-score of Multi-label Linear Discriminant Analysis followed by Binary Relevance K Nearest Neighbor (MLDA-BRKNN) achieved a more than 30% improvement in classification of the 1000 annotated images in the dataset when compared with the micro averages of precision, recall, and f1score of Binary Relevance K Nearest Neighbor (BRKNN), which was used as the reference classifier method in this study.