The COVID-19 pandemic created huge amounts of information about confirmed cases, deaths, testing, and vaccination worldwide and required the effective data processing and analysis system. The proposed project is a detailed data-oriented model to merge, process and visualize big data of COVID-19 to derive useful data. The suggested system suggests some of the major issues of the current methods, including the absence of standardisation, inconsistency of data sources, and fragmentation. The methodology consists in gathering various heterogeneous data and turn them into a single form using data cleaning, preprocessing and standardization procedures. The analysis of key epidemiological data, including the Case Fatality Rate (CFR) and the Test Positivity Rate, adds further insight to this system. Additionally, various visualization techniques, such as choropleth maps, bar charts, scatter plots and histograms, are used to represent complex data in a readily interpretable and understandable manner. The findings reflect the trends in the world, such as the unequal distribution of cases, differences in vaccination coverage, and testing and confirmed cases association. The suggested architecture proves to be more scalable, automated, and integrative, as opposed to current systems, which is why it is applicable in large-scale analysis and decision support. The system is also flexible to real-time data integration, and can be further expanded with predictive analytics to be used in the future in monitoring the health. On balance, the current project is efficient and viable to solve the problem of learning the dynamics of a pandemic and facilitate data-driven decision-making among researchers and analysts, as well as policymakers.
Bhattacharya et al. (Mon,) studied this question.