New Research on Dimensions of Uncertainty: A Spatiotemporal View of Five COVID-19 Datasets

Research scientists from the US COVID Atlas team at the Healthy Regions & Policies Lab (HEROP) are pleased to share their new article, Dimensions of Uncertainty: A Spatiotemporal View of Five COVID-19 Datasets, which was published this week in the journal Cartography and Geographic Information Sciences. Dylan Halpern, Qinyun Lin, and Marynia Kolak from HEROP and Steve Goldstein from University of Wisconsin-Madison co-authored the paper, along with UChicago student researchers Ryan Wang and Stephanie Wang. 

With so much necessary demand for COVID-19 data and data-driven research, their analysis seeks to explore and measure the similarities, differences, and uncertainties among five of the most popular sources of pandemic data, including Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres. Read on for the full abstract: 

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.

To learn more, read the full article and explore the data interactively in this ObservableHQ Notebook. You can also explore and compare multiple Covid data sources at the US Covid Atlas, HEROP’s award-winning open-source data visualization tool that features real-time and historical view of the pandemic through data.

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