Background: The outbreak of Covid-19 has created an emergency and dangerous situation for public health worldwide. Predicting the course of the disease and being aware of its future behavior will help managers and policymakers to have appropriate planning to control and modify effective social behaviors for the outbreak of the disease. Also, inventory management and order planning in medical centers are very necessary, especially during the pandemic crisis. It can significantly impact costs, provide optimal services to patients, and provide medical staff with essentials. Aim: The purpose of this research is to present a simulation data mining approach to investigate the behavior of the covid-19 epidemic at the same time as implementing the general vaccination plan and managing the hospital inventory during the epidemic crisis. Methodology: Based on the classification of paradigms of science, this research with the quiddity of scientific calculations with the approach of simulating a complex phenomenon is placed in the third paradigm of science, and with the essence of discovering knowledge from the big data of the Corona epidemic with the approach of data mining in this classification, it is in the field of the fourth paradigm of science, in this Research, the paradigm shift from the third paradigm to the fourth occurs. In other words, it can be said that at first, the forward-looking approach based on the complexity paradigm dominates the research, then by changing the paradigm and using big data, using data mining, and a retrospective approach based on observations, knowledge is discovered. Also, soft exploitation calculations with imprecise properties have been used to curb the problem and lower the cost of the solution based on the tolerance of inaccuracies, partial and incomplete facts, and lack of confidence. In this research, all three main components of soft computing, i. e. fuzzy theory, artificial neural networks, and meta-heuristic algorithms have been us