ShareTrace: An iterative message passing algorithm for efficient and effective disease risk assessment on an interaction graph
12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1365247
ABSTRACT
We propose a novel privacy-preserving COVID-19 risk assessment algorithm that can make a fundamental contribution to the development of the next generation resilient public health and health care systems. The proposed algorithm, ShareTrace, uses a hyperlocal interaction graph to capture direct and indirect physical interactions among users. Combining user-reported symptoms that are propagated through the hyperlocal interaction graph via a novel message passing algorithm, ShareTrace is able to pick up early warning signals based on the combination of interactions with others and symptoms. The proposed algorithm is inspired by the belief propagation algorithm and iterative decoding of low-density parity-check codes over factor graphs. Our evaluation on synthetic data shows the efficiency and efficacy of the proposed solution. © 2021 ACM.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2021
Year:
2021
Document Type:
Article
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