Blockchain-Based Trusted Tracking Smart Sensing Network to Prevent the Spread of Infectious Diseases

Background: Infectious diseases like COVID-19 pose major global health threats. Robust surveillance systems are needed to swiftly detect and contain outbreaks. This study investigates the integration of Blockchain technology and machine learning to establish a secure and ethically sound approach to tracking infectious diseases. Methods: We established a Blockchain-based framework for the collection and analysis of epidemiological data while upholding privacy standards. We employed encryption and privacy -enhancing technologies to gather information on case numbers, locations, and disease progression. Artificial neural networks were employed to scrutinize the data and pinpoint transmission patterns. A prototype was specifically designed to work with COVID-19 data from specific countries. Results: The Blockchain system enabled reliable and tamper -proof data gathering with enhanced transparency. The evaluation showed it allowed cost-effective tracking of infectious diseases while upholding confidentiality safeguards. The neural networks effectively modeled disease spread based on the Blockchain data. Conclusions: This research demonstrates the viability of Blockchain and machine learning for infectious disease surveillance. The system strikes a balance between public health concerns and personal privacy considerations. It also addresses the challenges of misinformation and accountability gaps during disease outbreaks. Ongoing development can lay the foundation for an ethical framework for digital disease tracking, ensuring both pandemic preparedness and response capabilities are upheld. (c) 2024 AGBM. Published by Elsevier Masson SAS. All rights reserved.