The third wave of the new variant of Coroid virus (Covid-19) Omicron has started in India.
Even today, on Monday, 1.8 lakh new patients were found. However, the recovery has been good today. About 50,000 people have survived the epidemic. IIT experts have predicted the duration of this third wave of terror in the world.
According to the Indian Express, Manindra Agarwal, Professor of Mathematics and Computer Science at IIT Kanpur, was asked how long the third wave of the corona would last and how long it would last. He has given free answers to many other questions.
In this regard, Professor Agarwal said, “We do not have enough data for the whole of India, but our estimate is that according to current calculations, the third wave could reach its peak in the middle of this month, early next month.” Corona parameters are changing rapidly. According to one estimate, we make a detailed estimate of four to eight million cases a day.
He further said that the faster the graph of Delhi and Mumbai goes up, the faster it is likely to come down. Cases are on the rise in other parts of India as well. It will take another month for this number to come down. The third wave of epidemics in India is expected to end by mid-March.
They were asked about the reliability of predictions of computer models. “It’s true that epidemics are a very fast-changing phenomenon in nature, but there are some basic principles,” he said. The infection spreads when an infected person comes in contact with a non-infected person. It is a simple analysis that the more infected people there are, the more new infections will occur.
The original model was created about 100 years ago. This is called the SIR model and it has proved to be very useful in predicting many contagious diseases. We’ve made some changes to this model, taking into account some local ground realities. In our model, we have allowed the parameters to learn their values from the input data. All we need is a daily time series of new cases. From that time series, we can estimate the required parameter values for our model.
This means that the parameter values should not change when we are guessing. If they change, our estimates will be wrong. It takes a while for the model to stabilize the parameters. Each time the parameters change, you have to recalculate. The good news is that apart from the input data, the model does not require any other calculations to calculate the parameter values.