Statistical Evaluation of Privacy-preserving Publication and Sharing of Three Types of COVID-19 Pandemic Data: Methods and Case Studies

A huge amount of data of various types are collected during the COVID-19 pandemic, the analysis and interpretation of which have been indispensable for curbing the spread of the coronavirus. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, na\"{i}ve release and sharing of the information can be associated with serious privacy concerns. In this study, we use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to demonstrate publication and sharing of granular information or individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. All the approaches employed in the study are straightforward to apply. We investigate the inferential utility of the privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the applications of approaches with examples and real-life pandemic data. Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.