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This means the number of ‘active’ cases in India will keep increasing roughly for another three weeks before a decline. If the current model shows the trend correctly, the mid-May peak would be three time higher than the first peak of over 10 lakh ‘active’ cases witnessed on September 17 last year.
The exercise is, however, important to prepare the policymakers for a proper response mechanism in terms of medical supplies and facilities.
The current model shows that Delhi, Haryana, Rajasthan and Telangana may see a peak of ‘new’ cases during April 25-30; Odisha, Karnataka and West Bengal during May 1-5 while Tamil Nadu and Andhra Pradesh during May 6-10. It shows Maharashtra and Chhattisgarh might already have reached its peak phase now while Bihar will see it around April 25.
“Our model shows a peak of cases of ‘new’ infections, which are being observed on a day-to-day basis, may be noticed during May 1-5 at about 3.3 to 3.5 lakh infections per day. It’ll turn the peak of ‘active’ cases to around 33-35 lakhs 10 days later between May 11-15,” Manindra Agrawal of IIT Kanpur, involved with the national ‘super model’ initiative, told TOI on Wednesday.
Though cases of Madhya Pradesh, Gujarat, Kerala and Goa are also being tracked, the model has not converged on them so the scientists would like to wait for a few more days to arrive at the prediction.
Referring to the current model, Agrawal said one should not confuse the two different peaks — one of daily ‘new’ cases which are more commonly observed and another of total number of ‘active’ infections which will come 10 days after the crest ‘new’ cases.
Earlier on April 1, the model had predicted the peak of ‘active’ cases somewhere between April 15-20 at around 10 lakh – the same level as what the country had seen in September last year. These figures, however, later kept on changing.
Asked about the reasons of such huge variation in the prediction which keeps on changing, Agrawal said, “The severity (of the Covid-19 spread) has made computations go haywire. We were seeing significant drift in parameter values for India in our model and so the (previous) modelling was not accurate.”
He noted that the parameter value keeps on changing due to new data from states and that’s why the peak value keeps on shifting. “The problem is that the parameters of our model for the current phase are continuously drifting. So, it is hard to get their value right,” said Agrawal.
Though the scientists know limitations of such predictions due to variation in data from a vast country like India, they cannot stop working on the model as such predictions, at least, provide some basic information to policymakers to finetune their response mechanism.
“Prediction gives you a fair estimate of what all you need (such as arrangement of hospital beds, ICUs, medical grade oxygen etc.) to do in the next one month or so. Though there is a risk of going wrong, we cannot stop doing it as such modeling is very important for preparing ourselves for the crisis,” said Agrawal.