Blockchain-based early warning system for infectious diseases: Integrating risk metrics for complex networks

• Published in: Journal of Autonomous Intelligence
• Main Author: Cai Y.; Ma J.; Zain J.M.; Wang Y.; Zheng M.
• Format: Article
• Language: English
• Published: Frontier Scientific Publishing 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185688617&doi=10.32629%2fjai.v7i4.651&partnerID=40&md5=010a5757578ad885b3029ed462cab8d8

The emergence of infectious diseases poses a significant threat to public health and requires prompt action to minimize their impact. Traditional infectious disease early warning systems (EDWS) face numerous challenges, including delays in reporting, data privacy concerns, and lack of transparency. Blockchain technology provides a promising solution to these challenges by offering a decentralized, secure, and transparent platform for data sharing and analysis. This paper analyzes cases, medical resources, pathological characteristics, and their connections using hospital data. It proposes an infectious disease risk measurement algorithm based on complex network modeling and integrates the risk measurement algorithm to construct a blockchain-based infectious disease risk early warning and data sharing system. The solution enables the safe storing and sharing of data using blockchain technology; performs distributed global model training via federated learning; and enables rapid and accurate infectious disease risk early warning by integrating smart contracts with physician expertise. Automatic reporting enhances infectious disease risk early warning and reporting. © 2024 by author(s).