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DTSTAMP:20260617T193824Z
DESCRIPTION:Click for Latest Location Information: http://edw2021.dataversi
 ty.net/sessionPop.cfm?confid=133&proposalid=12817\nCOVID-19 presents clear 
 challenges in both defining and capturing raw medical data, and moving that
  data along a very fragmented data-to-information supply chain to public he
 alth leadership. Managing the pandemic requires accurate data at both 
 the detail level (testing for infection, contact chasing, and quarantine) a
 nd high-quality expression of information for macro decision-making (for in
 stance, government and public health authorities). Definitions ne
 ed to be consistent and unambiguous (such as cause of death) yet are often 
 biased through political interference. \nThis presentation will look a
 t the various health care metrics, their weaknesses, and how they should be
  interpreted. “Cases” is a poor metric for the overall sit
 uation, when the sample is biased. We will propose new metrics, and discuss
  the impediments to gathering them. \nThe broader context of pandemic 
 data includes the points of failure in the data supply chain, and particula
 rly ineffective expression of derived data. \nIn this presentation you
  will learn:\n\n	Importance of precise definitions of medical events\n
 Spotting ambiguities in social and public health metrics\n
 Recognizing bias in data aggregation\n
 Techniques for manipulation of graphic expression\n\n
DTSTART:20210423T100000
SUMMARY:COVID-19 Case Study in Data Quality and Management
DTEND:20210423T104959
LOCATION: See Description
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