| dc.description.abstract | Supporting public health research in identifying disease disparities and developing targeted interventions, as well as enabling the public’s situational awareness during a pandemic, requires regular dissemination of infectious disease surveillance data. However, during the COVID-19 pandemic, privacy concerns have hindered data sharing on a broad scale. Even though legislation, such as the Health Insurance Portability and Accountability Act of 1996 and recent state-level regulations, permits sharing de-identified person-level data, 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 thesis, I introduce a framework to dynamically adapt de-identification for near-real time sharing of person- level surveillance data. I show how periodically adapting the data publication policies preserves privacy better than traditional de-identification methods while enhancing public health utility through timely updates and sharing epidemiologically critical features. I also show how the dynamic policy approach supports the early detection of underlying disparities better than traditional de-identification methods. As such, the dynamic policy approach has potential to publish data that supports a data-driven response in current and future pandemics. | |
