Federated Epidemic Surveillance
This addresses the challenge of infectious disease surveillance for public health institutions when data sharing is restricted, though it is incremental as it builds on existing federated and meta-analysis methods.
The study tackled the problem of epidemic surveillance with fragmented data by proposing a federated approach that uses hypothesis tests behind each custodian's firewall and combines p-values via meta-analysis techniques, achieving high fidelity in detecting outbreaks without sharing aggregate data.
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.