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Abstract Early racial disparities in COVID-19 vaccination rates have been attributed primarily to personal vaccine attitudes and behavior. Little attention has been paid to the possibility that inequitable vaccine distribution may have contributed to racial disparities in vaccine uptake when supplies were most scarce. We test the hypothesis that scarce vaccines were distributed inequitably using the shipping addresses of 385,930 COVID-19 vaccine doses distributed in the first 17 weeks of Pennsylvania’s Phase 1 rollout (December 14, 2020 through April 12, 2021). All shipments we analyze were allocated via the Federal Retail Pharmacy Program, a public-private partnership coordinated by the Centers for Disease Control and Prevention. Overall, White people had an average of 81.4% more retail pharmacy program doses shipped to their neighborhoods than did Black people. Regression models reveal that weekly vaccine allocations determined by pharmacy chains—rather than initial shipment and administration site decisions requiring state and federal approval—drove these effects. All findings remained consistent after controlling for neighborhood differences in income, population density, insurance coverage, number of pharmacies, and other social determinants of health. Our findings suggest that the private distribution of scarce public resources should be assessed for racial impact, regulated as public resources, and monitored continuously.

Abstract Planning and problem solving are cornerstones of higher brain function. But we do not know how the brain does that. We show that learning of a suitable cognitive map of the problem space suffices. Furthermore, this can be reduced to learning to predict the next observation through local synaptic plasticity. Importantly, the resulting cognitive map encodes relations between actions and observations, and its emergent high-dimensional geometry provides a sense of direction for reaching distant goals. This quasi-Euclidean sense of direction provides a simple heuristic for online planning that works almost as well as the best offline planning algorithms from AI. If the problem space is a physical space, this method automatically extracts structural regularities from the sequence of observations that it receives so that it can generalize to unseen parts. This speeds up learning of navigation in 2D mazes and the locomotion with complex actuator systems, such as legged bodies. The cognitive map learner that we propose does not require a teacher, similar to self-attention networks (Transformers). But in contrast to Transformers, it does not require backpropagation of errors or very large datasets for learning. Hence it provides a blue-print for future energy-efficient neuromorphic hardware that acquires advanced cognitive capabilities through autonomous on-chip learning.