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Related Experiment Video

Updated: May 15, 2025

Remote Laboratory Management: Respiratory Virus Diagnostics

Remote Laboratory Management: Respiratory Virus Diagnostics

Published on: April 6, 2019

Federated epidemic surveillance.

Ruiqi Lyu¹, Roni Rosenfeld², Bryan Wilder²

¹Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Plos Computational Biology

|April 8, 2025

Summary

This summary is machine-generated.

This study explores federated surveillance for epidemic tracking. Simple methods combining p-values can detect infectious disease outbreaks without sharing sensitive data.

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Last Updated: May 15, 2025

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Area of Science:

Epidemiology
Public Health
Data Science

Background:

Epidemic surveillance faces challenges due to fragmented data across institutions.
Data sharing is often hindered by privacy concerns or institutional unwillingness.
Effective surveillance requires methods that respect data locality.

Purpose of the Study:

To explore the feasibility of a simple federated surveillance approach.
To develop and test a hypothesis testing framework for detecting epidemic surges.
To assess p-value combination methods for privacy-preserving outbreak detection.

Main Methods:

Conducting hypothesis tests on count data within institutional firewalls.
Utilizing meta-analysis techniques to combine p-values from distributed tests.
Experimenting with real and semi-synthetic data to evaluate detection power.

Main Results:

Federated hypothesis testing effectively identifies surges in epidemic data streams.
Simple p-value combination methods demonstrate high fidelity in detecting outbreaks.
Surges can be detected without aggregating or sharing raw count data.

Conclusions:

Federated surveillance is a feasible approach for epidemic monitoring.
Infectious disease outbreaks can be detected while maintaining data privacy.
This method enhances public health surveillance capabilities.