As we are writing this paper, the number of daily affected COVID patients is around 0.38 million and with active cases over 3 million in India. This large number of active cases is putting the medical facilities under severe strain. Many researchers have proposed many ways of forecasting the COVID-19 patients but they mainly worked on the cumulative cases and moreover, all those methods required considerable skill and computational cost. In this work, a simple spreadsheet-based forecasting model has been developed which will help to predict the number of active cases in the immediate future i.e., the next few days. This information can be useful for emergency management. The difficulty which is generally faced in predicting the active cases is that the dynamics of active cases has a complex dependence on a number of Non-Pharmaceutical Interventions (NPI) and social factors and can undergo sharp changes. Quadratic, cubic and quartic polynomial functions have been applied to capture these peaks and observed that the quadratic function helps in better prediction of the peak. The accuracy of the prediction methods is measured as well as it is tried to observe how the methods predict data for the next 1 day, 3 days and 6 days. A prediction method analogous to weather forecasting method is recommended in this work where the prediction for each day gets updated depending on the most recent data available. This method has also been found to perform well even in the period there were sharp changes in the trend due to imposition of strict NPI measures.