An architecture for COVID-19 analysis and detection using big data, AI, and data architectures

Area of Science:

• Medical Informatics
• Data Science
• Cybersecurity

Background:

• The COVID-19 pandemic necessitates advanced diagnostic and management tools due to the lack of a permanent vaccine or cure.
• Existing technological approaches for COVID-19 analysis face challenges including data scarcity, inconsistency, and privacy concerns.

Purpose of the Study:

• To propose a novel, cooperative system integrating big data analytics, Artificial Intelligence (AI), and blockchain for analyzing and detecting COVID-19 instances.
• To develop a four-layered architecture for reliable data acquisition, storage, analysis, and reporting, ensuring data security and decentralization.

Main Methods:

• Utilizing big data analytics combined with AI techniques for preliminary diagnosis, affected area detection, and abnormality localization.
• Implementing a blockchain approach to ensure a decentralized, secure, and accessible data repository for all stakeholders.
• Designing a four-layered architecture for systematic data handling from acquisition to reporting within a decentralized P2P network.

Main Results:

• The proposed system aims to overcome limitations of current COVID-19 detection methods by addressing data reliability and security issues.
• The blockchain integration ensures decentralization, enhancing accessibility and stakeholder trust in the data repository.
• The four-layered architecture systematically manages data, providing a secure and reliable solution for pandemic analysis.

Conclusions:

• The developed system offers a reliable and secure technological solution for analyzing and detecting COVID-19, addressing critical data challenges.
• The integration of AI and blockchain technology provides a robust framework for pandemic management and future health crises.
• This research highlights the potential of advanced technologies in creating secure, decentralized systems for public health challenges.