Analysis of COVID-19 blockchain data using social signals for contact tracing

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Tadepalli, Sarada Kiranmayee
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Communities around the world are adapting to the fast-changing global situation due to the unprecedented effect of COVID-19. The data related to COVID-19 spread is being analyzed for identifying outbreaks and for trying to predict their future movement across geographies with the help of advanced machine learning models. The main challenge faced by researchers was using the centralized data sources, aggregating relevant data, and standardizing them, at a global level. In my thesis, I have first studied the difference between the centralized and blockchain-backed data provided by Mipasa (powered by the IBM Blockchain Platform and the IBM Cloud) and have developed a knowledge graph for COVID-19 cases in USA and Japan. The creation of a knowledge graph helped in predicting the regions which could witness the formation of new clusters. This observation helped in isolating regions and prevented the further spread of the virus. This led to the development of Decentralized Applications. The first decentralized application for COVID-19 symptoms tracking using Blockchain is developed to enhance reliable data collection for training Machine Learning (ML) models. The Blockchain integration in this application helped patients to provide COVID-19 symptoms data with trust. In addition to this, the data was first verified by an entity of the decentralized network (e.g. a COVID-19 testing lab). Then, with the consent of the patient, this data was provided to the centralized system for retraining the ML model. This re-training performed with verified data, updates the ML model and provides accurate results. The data collected from different platforms helped in identifying outbreaks, contact tracing, and the creation of a machine learning model for predicting the future movements of the outbreak to minimize the spread. However, there was delay in taking measures which cost many lives, and many local businesses were shut down around the world. As a solution to this problem, I have developed a Decentralized architecture where the Blockchain Oracle smart contract could access the data outside the Blockchain, the second application. The REST API provided the daily aggregated data from three platforms (Twitter, Google Mobility Data, and COVID-19 cases) for Toronto, Canada. This aggregated data helped participants of the Blockchain ecosystem to initiate the smart contracts early decisions like implementing lockdown, deploying officials for contact tracing, and providing financial support for businesses and individuals affected due to lockdown.
Blockchain, COVID-19, Blockchain data analysis
Sarada Tadepalli and Ruppa K. Thulasiram (2021), A Chapter in the Springer Nature book titled “Assessing COVID-19 and Other Pandemics and Epidemics” using Computational Modelling and Data Analysis; article title: Analysis of blockchain Backed Covid19 Data, (Ed. Subhendu K. Pani, Sujata Dash, Welling-ton PD Santos, Syed Ahmad Chan Bukhari, Francesco Flammini), pp.285- 299,
ICBA Paper: Sarada K. Tadepalli, Ruppa K. Thulasiram,COVID-19 Early Symptom Prediction Using Blockchain, and Machine Learning, 3rd International Congress on Blockchain and Applications, Salamanca, Spain, Springer Lecture Notes in Networks and Systems, Vol.320, pp.243-251, Oct. 2021