# Federated Epidemic Surveillance

arXiv: https://arxiv.org/abs/2307.02616 · HTML: https://mlsanity.com/paper/2307.02616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Choosing Between Methods of Combining p-values](https://mlsanity.com/paper/1707.06897/) (2017)
- [Online multiple hypothesis testing](https://mlsanity.com/paper/2208.11418/) (2022)

## Built on by (most cited first, 1 of 1)

- [Combining digital data streams and epidemic networks for real time outbreak detection](https://mlsanity.com/paper/2511.07163/) (2025)

## Abstract (arXiv)

Epidemic surveillance is a challenging task, especially when crucial data is fragmented across institutions and data custodians are unable or unwilling to share it. This study aims to explore the feasibility of a simple federated surveillance approach. The idea is to conduct hypothesis tests for a rise in counts behind each custodian's firewall and then combine p-values from these tests using techniques from meta-analysis. We propose a hypothesis testing framework to identify surges in epidemic-related data streams and conduct experiments on real and semi-synthetic data to assess the power of different p-value combination methods to detect surges without needing to combine the underlying counts. Our findings show that relatively simple combination methods achieve a high degree of fidelity and suggest that infectious disease outbreaks can be detected without needing to share even aggregate data across institutions.