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Data Analytics

Surveillance data on various notifiable diseases usually consist of multiple time series of daily, weekly, or monthly counts of new infections. Data are typically reported in several strata defined through administrative geographical areas, gender and/or age groups. Statistical modeling of the resulting multivariate time series is an important task in infectious disease epidemiology. We will discuss time series models - specifically developed for multivariate surveillance count data - that can be used for two distinct roles, understanding and prediction of disease spread.

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.

Presenters

To provide community input on data quality issues and enhance data quality through sharing and testing of scripts.

Summary of activities:

The Data Quality workgroup has worked to address Data Quality issues through the development, sharing and testing of scripts. The Data Quality workgroup formed a DQ EHR-Vendor Concern Subcommittee to address issues across vendors nationwide. 

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