ABSTRACT
An infectious disease outbreak will bring many different research concerns such as epidemiological, clinical, and analytical concerns. The research group will independently perceive the formulation of data-driven support models at population level. However, these projected models may be forecast by little information or parameters than these constructed models, which describe how the disease will spread, which group will be most severely affected in geographical region, or how certain interventions could impact the disease outbreak. Presently, there is very limited literature on the data-driven model on COVID-19 pandemic crisis. We have taken up this present research work and constructed advanced settings of models to describe the overall mechanism of disease spread and also to correlate clinical and epidemiological parameters. These reproducible results will help clinicians, epidemiologists, and policymakers to gain more knowledge and insights into the mechanism of the disease, prevention strategies, and vaccination drive, which can help mainly the public and policymakers in managing the pandemic crisis worldwide. The reader can easily grasp the model output and summarize clinical attributes for the extension of further microresearch. Our derived model has strong scientific underpinnings; they often differ greatly in their projections and policy recommendations.
