The ongoing epidemic SARS-CoV-2 named Corona Virus Disease (COVID-19) is highly infectious and subsequently spread all over the world affecting millions of people. Humans have never seen such a deadly disease so far, and as there is no specific drug or vaccination, the mortality rate of the disease has been increasing exponentially. This current situation exacerbated people’s restlessness and fear. Because of this pandemic, the world is travelling on a different path. This world has recovered from many disasters, but this is entirely a different situation. Today’s world is struggling in many ways to get rid of this disease. On the other hand, the number of people recovering from this disease gives us comfort. Yet, we have to take urgent precautionary measures to control this disease in all possible ways. Therefore, forecasting is one of the ways to take the necessary precautionary measures. In this paper, using fuzzy–grey–Markov model, we predict the number of affected and recovered patient count, death count using real-time data in different approaches and compared with the real data. The study concludes with important recommendations for the Indian government to manage the COVID 19 critical situation in advance.

COVID-19 is pandemic affecting most of the country globally. It is an infectious disease that is affecting most of the people and it is very difficult to diagnose and treat the diseased patient. Generally asymptotic patients recover without any treatment. Patients with other illness such as Hypertension, Heart and Lung problems, Diabetic patients require intense care and treatment. In such cases, a team of doctors work together. The combination of all the experts’ opinions is needed for efficient treatment. Often, the opinion of doctors depends on their experience and involves some differences. Further, the expert’s opinion is in linguistic terms. Plithogenic sets provide a mathematical tool for aggregation of the experts’ opinion expressed in linguistic terms. Thus, this work aims to employ plithogenic neutrosophic number to rank the diseased patients affected with COVID-19. Hence, we propose an Order Preference Technique by Similarity to Ideal Solutions (TOPSIS) using Plithogenic sets.

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