There is often a long path between the reality which raw data describes, and decision-maker understanding of what is going on (“big picture”). This “data-to-understanding supply chain” has many steps, with potential for lapses in the quality of data and information. This gap (with threats to quality) is particularly evident in the flow of Covid-19 data being passed around units of government in the U.S. The state and county totals suffer from a variety of definitions, inconsistent processes, and various sources of delay in reporting. This slide presentation provides simple explanations of the kinds of tests (antigen vs. antibody), and other issues of false negatives, comorbidity, and sampling techniques. Ambiguities abound. We shall also consider other new metrics which would be useful to understand this pandemic. We shall also touch on best practices (or ineffective techniques) in data visualization with some excellent examples (of both) from government sources.

View the presentation, Assessing Data Quality in the Covid-19 Metrics. A Case Study.