A Framework for Multi-source Privacy Preserving Epidemic Analysis

• URL: http://arxiv.org/abs/2506.22342v1
• Date: Fri, 27 Jun 2025 15:52:12 GMT
• Title: A Framework for Multi-source Privacy Preserving Epidemic Analysis
• Authors: Zihan Guan, Zhiyuan Zhao, Fengwei Tian, Dung Nguyen, Payel Bhattacharjee, Ravi Tandon, B. Aditya Prakash, Anil Vullikanti,
• Abstract summary: Some datasets are often sensitive, and need adequate privacy protections.<n>There are many models of privacy, but Differential Privacy (DP) has become a de facto standard because of its strong guarantees.<n>We develop a framework the integrates deep learning and epidemic models to simultaneously perform epidemic forecasting and learning a mechanistic model of epidemic spread.
• Score: 31.81847010151668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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