Data quality visualization for aggregate surveillance data with application to now-casting


This webinar will present a set of tools developed for visualizing data quality problems in aggregate surveillance data, in particular for data which accrues over a period of time. This work is based on a data quality analysis of aggregate data used for ILI surveillance within the Distribute system formerly operated by the ISDS. We will present a method developed as a result of this analysis to ‘nowcast’ complete data from incomplete, partially accruing data, as an example of how forecasting methods can be used to mitigate data quality problems.


Julie Eaton is an Assistant Professor of Mathematics in the School of Interdisciplinary Arts & Sciences at the University of Washington Tacoma. She earned her M.S. in statistics in 2009 and her Ph.D. in mathematics in 2010. She was involved with investigating the data quality associated with the ISDS Distribute data.  Her interests are in optimization, variational analysis, data quality visualization, and mathematical & statistical consulting.

Ian Painter is a Clinical Assistant Professor in the Northwest Center for Public Health Practice at University of Washington. He has a Ph.D. in Statistics from the University of Washington and over 15 years of experience consulting on research studies in the clinical, informatics and public health fields and is a current ISDS board member.

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March, 2016

March 13, 2017

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