Join the ASQ 1300 Denver Section to learn the interesting facts collected for this Covid-19 Case Study.
Managing a public health crisis requires reliable metrics, generally on an aggregate level. Unlike many industrial processes, raw data about a pandemic is captured through a variety of distributed locations, events, and jurisdiction. Data quality challenges abound. Definitions of events, metrics, and timing of aggregates create ambiguities and uncertainties.
We will examine critical elements of the current pandemic including explaining how a virus works, definitions of antigens, antibodies, co-morbidity, false negatives, and long-haul symptoms resulting from Covid-19 infection. We introduce a graphic model of the chain of events in a patient’s illness, and how we could benefit from more metrics with greater precision. We will introduce the concept of the event-to-information supply chain and issues of timing and ambiguity of high-level metrics. We show examples of political bias in expression and manipulation of information. We will finally look at the quality challenges of vaccine testing and distribution.
Michael Scofield, M.B.A. is an Assistant Clinical Professor at Loma Linda University in southern California. He is a popular speaker in topics of data management, data quality, and data visualization. His professional experience includes higher education, financial services, manufacturing, and software development. He has done over 300 lectures and workshops to professional audiences around the U.S., Canada, the U.K., and Australia. He also guest lectures at numerous universities in data management topics. He has over 27 articles published in journals dealing with data quality and database design.