Federated epidemic surveillance.
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. We conduct hypothesis tests on count data behin...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-04-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012907 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850200267660722176 |
|---|---|
| author | Ruiqi Lyu Roni Rosenfeld Bryan Wilder |
| author_facet | Ruiqi Lyu Roni Rosenfeld Bryan Wilder |
| author_sort | Ruiqi Lyu |
| collection | DOAJ |
| description | 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. We conduct hypothesis tests on count data 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 or share 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 or even aggregate data across institutions. |
| format | Article |
| id | doaj-art-bcdc53d4410d481a8b394c27f671cb96 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-bcdc53d4410d481a8b394c27f671cb962025-08-20T02:12:23ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-04-01214e101290710.1371/journal.pcbi.1012907Federated epidemic surveillance.Ruiqi LyuRoni RosenfeldBryan WilderEpidemic 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. We conduct hypothesis tests on count data 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 or share 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 or even aggregate data across institutions.https://doi.org/10.1371/journal.pcbi.1012907 |
| spellingShingle | Ruiqi Lyu Roni Rosenfeld Bryan Wilder Federated epidemic surveillance. PLoS Computational Biology |
| title | Federated epidemic surveillance. |
| title_full | Federated epidemic surveillance. |
| title_fullStr | Federated epidemic surveillance. |
| title_full_unstemmed | Federated epidemic surveillance. |
| title_short | Federated epidemic surveillance. |
| title_sort | federated epidemic surveillance |
| url | https://doi.org/10.1371/journal.pcbi.1012907 |
| work_keys_str_mv | AT ruiqilyu federatedepidemicsurveillance AT ronirosenfeld federatedepidemicsurveillance AT bryanwilder federatedepidemicsurveillance |