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Objectives: Federal open data initiatives that promote increased sharing offederally collected data are important for transparency, data quality, trust,and relationships with the public and state, tribal, local, and territorial(STLT) partners. These initiatives advance understanding of health conditionsand diseases by providing data to more researchers, scientists, andpolicymakers for analysis, collaboration, and valuable use outside CDCresponders. This is particularly true for emerging conditions such as COVID-19where we have much to learn and have evolving data needs. Since the beginningof the outbreak, CDC has collected person-level, de-identified data fromjurisdictions and currently has over 8 million records, increasing each day.This paper describes how CDC designed and produces two de-identified publicdatasets from these collected data.  Materials and Methods: Data elements were included based on the usefulness,public request, and privacy implications; specific field values were suppressedto reduce risk of reidentification and exposure of confidential information.Datasets were created and verified for privacy and confidentiality using datamanagement platform analytic tools as well as R scripts.  Results: Unrestricted data are available to the public through Data.CDC.govand restricted data, with additional fields, are available with a data useagreement through a private repository on GitHub.com.  Practice Implications: Enriched understanding of the available public data,the methods used to create these data, and the algorithms used to protectprivacy of de-identified individuals allow for improved data use. Automatingdata generation procedures allows greater and more timely sharing of data.

Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets for Public Use