Covid 19 has focused the world’s attention on health care facilities across the globe. Data modeling is a latent contributor for dynamic assessment and reporting/analyzing in distributed research networks. During our research, it was found that without demonstrative data, data sharing among subgroups focusing on various policy-making classifications, discoveries, and predictions become problematic and inefficient. The dearth of illustrative pandemic data (COVID-19) is a holdup for showcase and deliberate potential solutions. The realization of data for pioneering new technology in clinical healthcare systems and designing government decision support systems to help fight novel coronavirus is crucial. Dynamic graphs are a potential tool that aids in the dynamicassessment of country-wise policy data at a given point of time and is extremely significant for estimating public health measures and stringent policies. In the present research, the openly accessible Oxford’s COVID-19 Government Response Tracker (OxCGRT) dataset is used. The OxCGRT is counting statistics from more than 180 countries, it enables the investigators and officials to explore the pragmatic outcomes of policy responses on the accelerating spread of COVID-19, in addition to financial and communal welfare. The stringent policy index and Containment and health policy index is observed for the 11 Countries across the world with the highest occurrence of cases. This assortment of the sub-group countries reduces the complexity and upsurges the representativeness of data with respect to the model policy indicators.