# Improving Epidemic Analyses with Privacy-Preserving Integration of Sensitive Data

arXiv: https://arxiv.org/abs/2506.22342 · HTML: https://mlsanity.com/paper/2506.22342/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 68 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy](https://mlsanity.com/paper/2303.00654/) (2023)
- [Deep Learning with Label Differential Privacy](https://mlsanity.com/paper/2102.06062/) (2021)
- [Differential Privacy and Machine Learning: a Survey and Review](https://mlsanity.com/paper/1412.7584/) (2014)
- [LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis](https://mlsanity.com/paper/2108.06504/) (2021)
- [A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning](https://mlsanity.com/paper/2206.04621/) (2022)
- [Differentiable Agent-based Epidemiology](https://mlsanity.com/paper/2207.09714/) (2022)
- [DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis](https://mlsanity.com/paper/2010.01265/) (2020)
- [User-Level Differential Privacy With Few Examples Per User](https://mlsanity.com/paper/2309.12500/) (2023)
- [Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future](https://mlsanity.com/paper/2106.04420/) (2021)
- [Node Injection Link Stealing Attack](https://mlsanity.com/paper/2307.13548/) (2023)
- [Predicting seasonal influenza using supermarket retail records](https://mlsanity.com/paper/2012.04651/) (2020)
- [Differentially Private Partial Set Cover with Applications to Facility Location](https://mlsanity.com/paper/2207.10240/) (2022)

## Built on by (most cited first, 2 of 2)

- [Agentic Framework for Epidemiological Modeling](https://mlsanity.com/paper/2602.00299/) (2026)
- [Technical Case Study of Privacy-Enhancing Technologies (PETs) for Public Health](https://mlsanity.com/paper/2603.13444/) (2026)

## Abstract (arXiv)

Epidemic analyses increasingly rely on heterogeneous datasets, many of which are sensitive and require strong privacy protection. Although differential privacy (DP) has become a standard in machine learning and data sharing, its adoption in epidemiological modeling remains limited. In this work, we introduce DPEpiNN, a unified framework that integrates deep neural networks with a mechanistic SEIRM-based metapopulation model under formal DP guarantees. DPEpiNN supports multiple epidemic tasks (including multi-step forecasting, nowcasting, effective reproduction number (Rt) estimation, and intervention analysis) within a single differentiable pipeline. The framework jointly learns epidemic parameters from heterogeneous public and sensitive datasets, while ensuring privacy via input perturbation mechanisms. We evaluate DPEpiNN using COVID-19 data from three regions. Results show that incorporating sensitive datasets substantially improves predictive performance even under strong privacy constraints. Compared with a deep learning baseline, DPEpiNN achieves higher accuracy in forecasting and nowcasting while producing reliable estimates of Rt. Furthermore, the learned epidemic transmission models remain inherently private due to the post-processing property of differential privacy, enabling downstream policy analyses such as simulation of social distancing interventions. Our work demonstrates that interpretability (through mechanistic modeling), predictive accuracy (through neural integration), and rigorous privacy guarantees can be jointly achieved in modern epidemic modeling.