From the study, mathematical modelling helps to apply some computational techniques to predict the effect of CoViD-19 in each country. We started with the simple logistic model and, as expected, noticed that the model failed to predict the complete wave accurately. This is because of lots of unknown factors coming into picture during the initial stage of the pandemic. Then, we implemented the SIR model to fit the data and predict the effect. As noticed from the graphs, the basic SIR model was able to predict the waves with better accuracy than that of the logistic model. Though, we still had room for improvement by considering the human emotional factor into picture. This factor, known as Crowd effect, was considered where various emotions of humans were factored in to the mathematical equations and modelled accordingly. This model was the best among other models in terms of prediction of CoViD cases in countries.

The models predicted can be used to predict the future count of cases subject to nil interference from external factors such as government regulations. It must be noted that the models can also be used to analyse the situation and take remedial actions. This was the case mostly in many countries at the time of modelling, where the impact was assessed critically and measures were taken in-order to control the variables which are heavily impacting the count of total cases. This is where the essence of our modelling lies, where the heavily impacting variables are identified and controlled. One such variable of heavy impact is the rate of spread of infection which was very much integral in determining the spread of pandemic.