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Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne

arXiv:2106.14631v1cs.CReess.SP

TL;DR

COVID-19 exposed gaps in surveillance, data reliability, testing speed, and large-scale healthcare analytics. The paper surveys blockchain and AI, introduces an integrated architecture, and includes a federated-AI detection case study; the advanced scheme reports 0.983 COVID19 precision. The survey also identifies the lack of unified databases as a critical challenge.

  • Problem

    COVID-19 healthcare systems lack reliable surveillance, accurate and rapid detection, and capacity to process complex, large-volume data.

  • Method

    The paper surveys blockchain and AI research, introduces an integrated architecture, reviews applications and use cases, and presents a federated-AI COVID-19 detection case study.

  • Results

    0.983 precision for the COVID19 class was reported by the advanced FL scheme, exceeding standalone, GAN-based, and FL schemes at 0.891, 0.914, and 0.950.

  • Takeaways & Limitations

    The survey identifies blockchain and AI applications spanning outbreak tracking, privacy protection, medical supply chains, detection, analytics, and drug or vaccine development.

  • Takeaways & Limitations

    The survey's practical scope is constrained by the lack of unified coronavirus databases and countries' reluctance to share disaggregated data.

Abstract

from arXiv · show

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

I. INTRODUCTION

The paper motivates blockchain and AI as complementary responses to weaknesses in pandemic healthcare systems. Blockchain can improve data integrity and coordination, while AI supports outbreak analysis, diagnosis, forecasting, and drug development.

  • Healthcare-system limitations: Current healthcare systems lack reliable real-time surveillance, rapid accurate testing, and tools for processing complex, large-scale coronavirus data.These limitations hinder outbreak identification, quarantine, and timely analysis.
  • Blockchain benefits: Blockchain can support COVID-19 response through immutable medical-data records, reliable data collection, outbreak detection, drug delivery, and incentive-based crowdsensing.Its immutability makes recorded transactions harder to alter, while incentive mechanisms can encourage information sharing and monitoring.
  • AI benefits: AI can learn from epidemiological and environmental datasets to forecast cases and potential outbreaks.The paper also identifies AI applications in monitoring real-time outbreaks and supporting coronavirus analytics.
  • AI benefits: AI-based computation can model coronavirus structures and estimate which drugs may work against COVID-19.The paper presents machine support as necessary for processing the massive databases involved in these tasks.
  • Motivation: Blockchain and AI are presented as viable technologies for addressing the coronavirus epidemic from multiple aspects.The introduction frames their use as a response to the worldwide health crisis and its broader disruptions.
  • Prior evidence: Prior epidemic applications used blockchain for Ebola contact tracing and vaccine delivery, while AI supported influenza forecasting and Ebola compound discovery.These examples motivate applying both technologies to coronavirus-like epidemics.

B. COMPARISONS AND OUR CONTRIBUTIONS

The paper distinguishes its work from narrower prior surveys by jointly reviewing blockchain and AI for COVID-19. It contributes an integrated architecture, application and use-case review, a federated-AI case study, and identified research directions.

  • Comparisons: Earlier surveys separately examined blockchain or AI roles in COVID-19, healthcare management, crisis trust, and selected analytics or development domains.The cited works vary from brief discussions to broader technology-specific surveys.
  • Our contributions: This paper provides a comprehensive review of both blockchain and AI applications and use cases for fighting the COVID-19 pandemic.It uses emerging literature and recent research reports to provide an overall picture and roadmap.
  • Our contributions: A federated-AI case study is provided for COVID-19 detection, alongside challenges and future directions for future coronavirus-like epidemics.The case study is intended to demonstrate the benefits of the discussed technique in the pandemic.
  • Our contributions: The paper introduces a conceptual systematic architecture integrating blockchain and AI for responding to the coronavirus epidemic.The architecture is intended to provide key solutions for coronavirus fighting.
  • Our contributions: The survey identifies specific applications, reviews emerging projects and use cases, and analyzes the technologies across applied coronavirus-related scenarios.These contributions emphasize both application breadth and practical examples.

C. METHODS AND MATERIALS

The paper uses a systematic mapping study to survey blockchain and AI research for COVID-19. It organizes the review around background, technology solutions, use cases, a federated-AI case study, and challenges.

  • Methods: The survey adopts a systematic mapping study as its research method to overview blockchain and AI research for fighting COVID-19.The method begins by identifying healthcare-system limitations and motivations for using the technologies, then searches relevant scientific papers.
  • Organization: The paper presents background on the coronavirus epidemic, blockchain, and AI before introducing an integrated architecture for coronavirus fighting.This material is covered in the background section.
  • Organization: The review then analyzes blockchain solutions, AI applications, and popular blockchain-and-AI use cases for coronavirus response.The organization separates technology-specific analyses before combining them in use-case discussions.
  • Organization: A federated-AI COVID-19 case study demonstrates the feasibility of the paper's approach, followed by challenges and future directions.The later sections address implementation issues and research opportunities.

B. BLOCKCHAIN

Blockchain is presented as a decentralized, consensus-based ledger for ordered and resistant-to-modification records, with smart contracts and IoT integration supporting secure healthcare data exchange. The paper then proposes integrating blockchain with AI across data sources, blockchain functions, AI functions, and stakeholders for coronavirus fighting.

  • Blockchain fundamentals: Blockchain distributes records across network participants and uses consensus to agree on the ledger’s status.Public blockchains are permissionless, whereas private blockchains are invitation-only and centrally managed.
  • Blockchain fundamentals: Transactions form blocks, whose linked hashes and timestamps preserve the chronological order of medical and COVID-19 data records.Cryptographic signatures make distributed-ledger records resistant to modification.
  • Blockchain fundamentals: Smart contracts are programmable blockchain applications whose executions produce transparent, consistently recorded results across network nodes.They can reduce reliance on external authorities for contractual operations.
  • Blockchain fundamentals: Healthcare IoT sensors and gateways can submit real-time patient data to blockchain for secure sharing and storage without a central authority.This connects decentralized blockchain communication with biomedical operations.
  • Proposed blockchain-AI architecture: The proposed architecture integrates coronavirus data sources, blockchain functions, AI functions, and stakeholders into a conceptual workflow.Data from clinical labs, hospitals, social media, and other sources are consolidated into coronavirus big data before blockchain security and AI analysis.
  • Proposed blockchain-AI architecture: The survey reviews blockchain and AI research, projects, and use cases for coronavirus epidemics, including federated AI for COVID-19 detection.The architecture uses blockchain for secure data exchange and AI tools such as neural networks for analytics including classification and regression.

III. BLOCKCHAIN-BASED SOLUTIONS FOR CORONAVIRUS FIGHTING

The paper analyzes blockchain’s role in coronavirus fighting through four key solution areas: outbreak monitoring, safe day-to-day operations, medical supply chain, and donation tracing.

  • Blockchain-based solutions: Blockchain-based coronavirus solutions are organized around outbreak monitoring, safe day-to-day operations, medical supply chain, and donation tracing.These four areas define the section’s framework for analyzing blockchain applications.

A. OUTBREAK MONITORING

Blockchain is described as a tool for outbreak monitoring and safer operations by recording data immutably, supporting verification, and enabling virtual services and payments. The section also reports a blockchain trial processing 2,919 document types faster than traditional approaches.

  • Outbreak monitoring: Blockchain ledgers can record patient infection symptoms immutably, supporting real-time monitoring and reducing opportunities to deliberately misreport symptoms.The paper links reliable symptom records with monitoring coronavirus spread and quarantine compliance.
  • Outbreak monitoring: Fake news on social media and websites creates confusion, fear, panic, racist vigilantism, and scapegoating during the pandemic.The paper identifies verification and authentication of pandemic information as a needed response.
  • Safe day-to-day operations: Blockchain-based virtual environments can support government services without requiring visits to offices and service centres.The UAE Ministry of Community Development used blockchain channels for digital authentication of official certificates and documents.
  • Safe day-to-day operations: 2,919 different types of documents were processed in the UAE blockchain trial, reportedly faster than traditional working approaches.The reported result concerns digital government-document processing.
  • Safe day-to-day operations: Electronic payment via blockchain is presented as an alternative to banknotes during the coronavirus crisis, while decentralized bidding is described as transparent and trustworthy.The paper frames these applications as contact-reducing virtual economic activities.

C. MEDICAL SUPPLY CHAIN

Blockchain is presented as a way to improve medical-supply continuity by reliably tracking demand, goods, transport, payments, and certifications. An Alipay platform in China is cited as a use case for tracking medical-supply demand and supply chains.

  • Medical supply chain: Blockchain can track medical supplies from origins to destinations, supporting reliable flow during pandemic-related shortages.The paper emphasizes continuity of medicines and food supplies as a healthcare-sector challenge.
  • Medical supply chain: An Alipay platform launched with Chinese health and technology authorities tracks demand and supply chains for masks, gloves, and other protective gear.The company claims secure block and transaction links provide high traceability and fast data flow.
  • Medical supply chain: Real-time demand updates and medical-factory information can support rapid supply-chain responses such as rate adjustment.This is listed as a product-requirements function.
  • Medical supply chain: Blockchain supply-chain records can support supply credibility by controlling factory-side product specifications and supply volumes.The paper lists this as a solution for verifying supply information.
  • Medical supply chain: Transaction recording and monitoring can trace transportation, while timestamped and signed records support financial payments and customs certifications.These functions are presented as components of transparent medical-supply operations.
  • Medical supply chain: Blockchain can trace donations and medical support so transferred goods or money can be connected to targeted patients and victims.The paper also describes COVID-19 data as timestamped and immutably tracked on a blockchain database platform.

IV. AI-BASED SOLUTIONS FOR CORONAVIRUS FIGHTING

The survey describes five AI applications for coronavirus response, including outbreak estimation, detection, analytics, vaccine and drug development, and future-outbreak prediction. It also highlights AI analysis of mobile-phone usage and movement patterns to estimate outbreaks and identify potentially exposed people.

  • AI applications for coronavirus response include outbreak estimation, detection, analytics, vaccine and drug development, and prediction of future coronavirus-like outbreaks.
  • Outbreak estimation: AI can estimate outbreak size by analyzing changes in people’s phone-usage patterns during illness, death, caregiving, or lockdown.
  • Outbreak estimation: Machine learning models personalized user activities from phone records, while deep learning targets accurate prediction of abnormal calling and phone-service behavior.
  • Outbreak estimation: Mobile operators can analyze cellular-location and movement patterns to identify crowding, define quarantine areas, and trace contacts of infected passengers.

B. CORONAVIRUS DETECTION

The surveyed detection and treatment studies apply AI to face, breathing, genome, CT, and antibody data. Reported approaches include high classification performance, infection-region segmentation, genome discrimination, and machine-learning generation of antibody candidates.

  • Detection methods: AI-based detection studies examine facial temperature, mask status, breathing characteristics, viral genomes, and thoracic CT images.
  • Detection methods: 98% accuracy: ResNet50 achieved the highest classification performance among three proposed models in simulations.
  • Detection methods: A deep convolutional network classified SARS-CoV-2 from genome sequences and distinguished it from MERS-CoV and SARS-CoV using the 2019nCoVR dataset.
  • Diagnosis and treatment: Thoracic CT studies use 2D and 3D deep-learning models, clinical information, and location-attention classification to diagnose and quantify COVID-19 infection.
  • Diagnosis and treatment: Machine learning generated thousands of potential antibody candidates from 1933 virus-antibody sequences and clinical IC50 data.
  • Diagnosis and treatment: AI-supported vaccine and drug research predicted 24 vaccine candidates and identified commercially available drugs or FDA-approved compounds for testing against COVID-19.

E. PREDICTION OF FUTURE COVID-19 OUTBREAK

The survey covers AI models for estimating COVID-19 outbreak size, duration, and ending time, identifying factors associated with zoonotic outbreaks, modeling stochastic epidemic development, and predicting virus hosts. It also presents federated learning as a privacy-protective approach for COVID-19 analytics.

  • Prediction of future COVID-19 outbreak: AI prediction models estimate COVID-19 outbreak size, duration, and ending time across China.
  • Prediction of future COVID-19 outbreak: Machine learning combined with causal inference identifies and quantifies factors associated with zoonotic disease and COVID-19 outbreaks.
  • Prediction of future COVID-19 outbreak: Deep learning and fuzzy-rule induction model nondeterministic data distributions to provide stochastic insight into epidemic development and future outbreak possibility.
  • Federated AI: Federated learning addresses limited data availability and privacy concerns by enabling distributed COVID-19 analytics without centralizing local data.
  • The survey presents blockchain and AI projects and use cases as responses to the coronavirus pandemic.

A. BLOCKCHAIN USE CASES

The surveyed use cases apply blockchain to outbreak logging, donation tracking, vaccine-production records, and community verification. AI projects support outbreak detection, clinical diagnosis, protein-structure prediction, and privacy-preserving multinational CT analysis.

  • Blockchain use cases: HashLog uses a distributed ledger to log and visualize coronavirus outbreak data from CDC and WHO sources, with potentially real-time transmission updates.
  • Blockchain use cases: Hyperchain tracks donations transparently from origin to destination and connects users with donated goods and medical equipment.
  • Blockchain use cases: VeChain records vaccine-manufacturing activities on distributed ledgers to reduce modification risks and preserve immutable vaccine information.
  • Blockchain use cases: PHBC anonymously verifies virus-free communities and workplaces while monitoring uninfected-person movement and restricting returns from infected areas.
  • AI use cases: Bluedot combines social media, government, and healthcare data with NLP and machine learning to track outbreaks of over 100 diseases every 15 minutes.
  • AI use cases: Infervision accelerated CT diagnosis at Tongji Hospital, while AlphaFold predicted structures of under-studied SARS-CoV-2 proteins and remained experimentally unverified.
  • AI use cases: Federated AI combined 1704 scans from China, Italy, and Japan to support collaborative COVID-19 CT segmentation without sharing patient information.

C. COMBINING BLOCKCHAIN AND AI FOR COMBATING

The paper combines blockchain and AI in epidemic-response applications and presents a federated AI case study for COVID-19 detection. The case study uses distributed X-ray data to train a global GAN, whose synthetic images improve classification performance.

  • A blockchain-and-AI application predicts COVID-19 pandemic evolution and uses digital identities and licenses to track compliance with social-distancing rules.
  • The Mateon–Meridian IT system combines AI-based neural networks for automated drug production with blockchain-based FDA compliance monitoring.The proposed workflow targets faster, more accurate, and more reliable drug manufacturing for timely COVID-19 supply.
  • The case study develops a federated AI approach using federated learning for COVID-19 classification across medical institutions and a cloud server.Each institution contributes its own X-ray dataset while participating in the federated process.
  • The experiment uses 620 X-ray images across COVID-19, normal, and pneumonia classes at five institutions, generating 1500 synthetic images for classification.A three-convolutional-layer CNN with Adam optimization evaluates the augmented data against state-of-the-art schemes.
  • Federated GAN training achieves lower discriminator loss than standalone training by learning across the complete distributed dataset.The authors associate access to the full data span with better synthetic-image features for data augmentation.
  • For the COVID-19 class, the federated scheme achieves the best precision at 0.983 and also outperforms competing schemes in sensitivity and F1-score.The comparison includes standalone, GAN-based, and federated alternatives with lower precision values of 0.891, 0.914, and 0.950, respectively.

VII. CHALLENGES AND FUTURE DIRECTIONS

The survey identifies regulatory, privacy, security, database, implementation, and data-integration challenges for blockchain and AI in epidemic healthcare. These issues constrain reliable deployment and motivate performance, governance, and infrastructure improvements.

  • Regulatory Consideration: Regulatory uncertainty requires accountable parties, applicable transaction law, risk management, and governance for blockchain and AI healthcare operations.The discussion also raises copyright, personal-information, and defamation concerns.
  • People’s Privacy Preservation: Privacy-preserving coronavirus tracking must protect sensitive location and healthcare data while reconciling data collection with user privacy.Sensitive information includes home addresses, banking details, and shopping records.
  • Security of Blockchain and AI Ecosystem: Blockchain and AI healthcare ecosystems retain security weaknesses, including adversarial control that could enable harmful medical-data modifications.The survey cautions that blockchain security assumptions do not eliminate risks in medical applications.
  • Lack of Unified Databases: The lack of unified epidemic databases limits large-scale AI operations and prevents WHO from evaluating outbreaks without disaggregated local information.Relevant data include infected cases, affected areas, and medical supply status.
  • Implementation Challenges: Blockchain deployment faces hardware, storage, energy, latency, and throughput constraints in distributed healthcare networks, especially with resource-constrained IoT devices.Bitcoin processes up to 4 transactions/second, Ethereum about 20, while Visa processes up to 1667.
  • Implementation Challenges: Growing AI-related data volumes increase the need for integrated big-data platforms supporting COVID-19 analytics, monitoring, and research tools.The survey links this need to advances in analytic techniques and big-data activities.

B. FUTURE DIRECTIONS

The survey proposes future work spanning blockchain performance and security, specialized AI analytics, and integration with complementary technologies. It concludes by positioning these directions within a broader survey of blockchain–AI applications for COVID-19 and future epidemics.

  • Performance Improvement of Blockchain: Blockchain platforms should improve scalability, resource consumption, throughput, and network latency for emergency healthcare applications.The survey specifically calls for scalable, lightweight designs that optimize data verification and transaction processing.
  • Security Issues of Blockchain: Future blockchain research should address 51% and double-spending attacks through innovative security mechanisms, including mining-pool strategies.The cited mining-pool strategy targets mining efficiency and the 51% vulnerability.
  • Improved AI Algorithm for Better Analytic Accuracy: AI research should develop medical architectures and adaptive models capable of analyzing multimedia healthcare data during COVID-19-like emergencies.Suggested applications include predictive modeling and pandemic-oriented analytics.
  • Combination with Other Technologies: Blockchain and AI can be combined with cloud computing and other technologies to build more comprehensive healthcare systems.The survey cites Alibaba’s integration of AI with cloud computing for coronavirus data analytics.
  • Conclusions: The paper surveys blockchain and AI applications for COVID-19, introducing an integrated architecture, application domains, use cases, a case study, challenges, and future directions.Blockchain domains include outbreak tracking, privacy, operations, medical supply chains, and donation tracking; AI domains include estimation, detection, analytics, drug development, and future-outbreak prediction.
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