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COVID-19 surveillance across the United States is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen's kappa) and agreement across all datasets (Fleiss' kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic. [ABSTRACT FROM AUTHOR]

We examine differences in mobility outcomes between residents of highest and lowest socio-economic index (SEI) at the Census block group (CBG) level in nine major US cities prior to and during the COVID-19 pandemic. While low-SEI groups generally traveled shorter distances but visited more city-wide CBGs before the pandemic, high-SEI residents universally reduced their mobility to a greater extent during the pandemic. Although high-SEI residents were making more trips to parks and health-care providers, and fewer subsistence trips to retail stores already before the pandemic, COVID-19 significantly widened these differences thereby exacerbating "mobility gaps" between low-SEI and high-SEI groups. We further examine how such "mobility gaps" can be mitigated by spatial advantages of home locations, controlling for political inclination. We find that living in better transit-served or more walkable neighborhoods generally benefited high-SEI residents more than low-SEI residents, with some variation across cities. This suggests that built environments not only impact mobility outcomes during "normal" times, but also influence how different socio-economic groups are able to adapt during times of crisis. • We analyze pre- and during COVID-19 mobility behavior by socio-economic status based on over 139 million individual trips in nine U.S. cities. • High-SEI residents universally reduced their mobility to a greater extent during the pandemic. • We find a broad shift to automobile travel in transit-oriented areas during the pandemic. • Neighborhoods characteristics such as walkability and transit accessibility impact travel outcomes beyond income or socio-economic status. • Unaffordability of neighborhood amenities in walkable urban districts could push low SEI residents to travel further to more affordable destinations. [ABSTRACT FROM AUTHOR]