Federated Epidemic Surveillance

TL;DR

This paper presents a federated approach for epidemic surveillance that detects outbreaks by combining p-values from hypothesis tests conducted locally, avoiding data sharing across institutions.

Contribution

It introduces a simple federated framework using p-value combination techniques for outbreak detection without sharing raw or aggregate data.

Findings

High detection fidelity with simple p-value combination methods

Effective outbreak detection without sharing underlying counts

Framework applicable to real and semi-synthetic data

Abstract

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.