A Framework for Multi-source Privacy Preserving Epidemic Analysis
This work addresses the challenge of leveraging diverse, privacy-sensitive data for public health applications, representing an incremental advance by applying existing privacy methods to a new type of dataset in epidemic analysis.
The paper tackles the problem of integrating multiple sensitive datasets for epidemic analysis while preserving privacy, using a framework that combines deep learning and epidemic models to perform forecasting and learn mechanistic models, showing that even with differential privacy guarantees, a synthetic financial dataset adds significant value.
It is now well understood that diverse datasets provide a lot of value in key epidemiology and public health analyses, such as forecasting and nowcasting, development of epidemic models, evaluation and design of interventions and resource allocation. Some of these datasets are often sensitive, and need adequate privacy protections. There are many models of privacy, but Differential Privacy (DP) has become a de facto standard because of its strong guarantees, without making models about adversaries. In this paper, we develop a framework the integrates deep learning and epidemic models to simultaneously perform epidemic forecasting and learning a mechanistic model of epidemic spread, while incorporating multiple datasets for these analyses, including some with DP guarantees. We demonstrate our framework using a realistic but synthetic financial dataset with DP; such a dataset has not been used in such epidemic analyses. We show that this dataset provides significant value in forecasting and learning an epidemic model, even when used with DP guarantees.