  Supporting public health research and the public's situational awareness
during a pandemic requires continuous dissemination of infectious disease
surveillance data. Legislation, such as the Health Insurance Portability and
Accountability Act of 1996 (HIPAA) and recent state-level regulations, permits
sharing de-identified person-level data; however, current de-identification
approaches are limited. namely, they are inefficient, relying on retrospective
disclosure risk assessments, and do not flex with changes in infection rates or
population demographics over time. In this paper, we introduce a framework to
dynamically adapt de-identification for near-real time sharing of person-level
surveillance data. The framework leverages a simulation mechanism, capable of
application at any geographic level, to forecast the re-identification risk of
sharing the data under a wide range of generalization policies. The estimates
inform weekly, prospective policy selection to maintain the proportion of
records corresponding to a group size less than 11 (PK11) at or below 0.1.
Fixing the policy at the start of each week facilitates timely dataset updates
and supports sharing granular date information. We use August 2020 through
October 2021 case data from Johns Hopkins University and the Centers for
Disease Control and Prevention to demonstrate the framework's effectiveness in
maintaining the PK!1 threshold of 0.01. When sharing COVID-19 county-level case
data across all US counties, the framework's approach meets the threshold for
96.2% of daily data releases, while a policy based on current de-identification
techniques meets the threshold for 32.3%. Periodically adapting the data
publication policies preserves privacy while enhancing public health utility
through timely updates and sharing epidemiologically critical features.
