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The visualization of accurate coronavirus disease – covid-19 data and information is vital in a public-related health predicament since the general public’s attempt to make individual meaning of its vaccination and mortality trends may lead to a widespread misrepresentation and misinterpretation that could affect the goal of information dissemination and collective actions. The
present study frowned towards the variations in covid-19 information across reporting dashboards, and in turn, utilized the efficiency of machine learning techniques in the appropriation of multiple datasets for representing covid-19 information via a dashboard. Using the CRISP-DM methodology, data aggregation and visualization techniques were employed to promote interactive representation of covid-19 pandemic progression across several countries for the general public, government agencies, and health/research organizations who inevitably and dynamically pursue the representation of unified and accurate covid-19 information. The results, using the UK as a case study, visualized the aggregated vaccination and mortality progression rate with the prevalence of accurate covid-19 infected cases and mortality relative to vaccinated persons on a global scale. The study advises from the results and findings that countries with a high incidence rate of covid-19 cases should be more vaccine-oriented with collective actions to reduce the number of deaths associated with the covid-19 plague.