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This means that the number of “active” cases in India will continue to rise for about another three weeks before the decline. If the current model shows the trend correctly, the peak in mid-May would be three times higher than the first peak in over 10 lakh of “active” cases witnessed on September 17 last year.
The exercise is, however, important to prepare policy makers for an appropriate response mechanism for medical supplies and facilities.
The current model shows that Delhi, Haryana, Rajasthan and Telangana could see a peak of “new” cases between April 25-30; Odisha, Karnataka and West Bengal from May 1-5, while Tamil Nadu and Andhra Pradesh from May 6-10. It shows that Maharashtra and Chhattisgarh could have already reached its peak now, while Bihar will see it around April 25.
“Our model shows a peak of cases of“ new ”infections, which are observed every day, can be observed between May 1-5, at about 3.3 to 3.5 lakh infections per day. It will turn the peak of “active” cases around 33-35 lakhs 10 days later between May 11-15, “TOI Manindra Agrawal of IIT Kanpur, involved in the national” super model “initiative, said on Wednesday.
Although the cases in Madhya Pradesh, Gujarat, Kerala and Goa are also being followed, the model has not converged on them, so scientists would like to wait a few more days to reach the prediction.
Referring to the current model, Agrawal said that we should not confuse the two different peaks – one of the “new” daily cases that are more frequently observed and another with the total number of “active” infections that will occur 10 days after ridge ‘cases.
At the beginning of April 1, the model predicted the peak of “active” cases somewhere between April 15-20, at about 10 lakh – the same level as what the country saw in September last year. However, these figures continued to change thereafter.
Asked about the reasons for such a huge variation in the ever-changing prediction, Agrawal said: “The severity (the spread of Covid-19) made the calculations unwavering. We observed a significant drift of parameter values for India in our model, so the (previous) modeling was not accurate. ”
He noted that the value of the parameter continues to change due to new data from the states and therefore the peak value continues to change. “The problem is that the parameters of our model for the current phase are in continuous derivation. So, it is difficult to get the right value for them “, said Agrawal.
Although scientists know the limits of such predictions due to the variation of data in a vast country like India, they can not stop working on the model, because such predictions, at least, provide some basic information to decision makers to perfect the response mechanism.
“The prediction gives you a correct estimate of what you need (such as arranging hospital beds, ICU, medical grade oxygen, etc.) to do in the next month or so. Although there is a risk of making a mistake, we cannot fail to do so, because such modeling is very important to prepare for the crisis, ”said Agrawal.