Covid-19 data visualization using tableau - Rudy Tantowi; Calvin Calvin; Saut Dohot Siregar (Institute of Accounting Research and Novation (IARN), 2025)
The COVID-19 pandemic crisis resulted in the accumulation of extensive epidemiological data that demanded the implementation of advanced visualization techniques to support community health surveillance systems. This research adopts the Tableau platform in the development of a dynamic dashboard for a holistic examination of COVID-19 data. A quantitative-descriptive methodological approach was applied using secondary databases from global repositories covering parameters of cases, fatalities, morbidity, and territorial distribution. The construction of the dashboard consolidates chronological-geographical visualization, predictive analytics, and assessment of vaccination efficiency. The findings indicate the superior capability of Tableau in processing epidemiological big data with optimal performance metrics. Temporal investigation identified recurring patterns with different wave characteristics, while geographical mapping exposed the epicenters of transmission and propagation paths. The forecasting model achieved high precision at near-term horizons (MAPE 8.45% for 7-day prediction). Vaccination evaluation displayed a constructive correlation between coverage levels and the suppression of incidence. Analysis of user experience confirmed preferences for an interface that is user-friendly with sophisticated analytical capabilities. This study contributes academically by enriching the literature on health data visualization and practically by offering a dashboard model that supports real-time public health decision-making.