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This means that the number of ‘active’ cases in India will increase roughly for another three weeks before a downturn. If the current model correctly shows the trend, the mid-May peak would be three times higher than the first peak of over 10 lakh ‘active’ cases seen on September 17 last year.
The exercise, however, is important in preparing the policy makers for a proper response mechanism for medical supplies and facilities.
The current model shows that Delhi, Haryana, Rajasthan and Telangana could 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 that Maharashtra and Chhattisgarh may already have reached its peak period now while Bihar will see it around April 25.
“Our model shows a peak of ‘new’ infections, which are observed on a daily basis, during May 1-5 at around 3.3 to 3.5 lakh infections per day. It will turn the peak of ‘active’ cases to around 33-35 lakh 10 days later between May 11-15, ”Manindra Agrawal of IIT Kanpur, involved in the national ‘senior model’ initiative, told TOI on Wednesday .
While cases from Madhya Pradesh, Gujarat, Kerala and Goa are also being tracked, the model has not converged on them so the scientists would like to wait another few days to reach the prediction.
Referring to the current model, Agrawal said one should not confuse the two different peaks – one of the more commonly observed daily ‘new’ cases and another of the total number of ‘active’ infections that occur 10 days after the ‘new’ crest cases.
Earlier on April 1, the model had predicted a peak of ‘active’ cases somewhere between April 15-20 at around 10 lakh – the same level as the country experienced in September last year. However, these figures continued to change later.
Asked about the reasons for such a huge variation in the ever-changing prediction, Agrawal said, “Severity (Covid-19 spread) has made computations go haywire. We were seeing a significant drift in India parameter values in our model and therefore (previous) modeling was not accurate. ”
He noted that the parameter value continues to change due to new data from states which is why the peak value continues to shift. “The problem is that the parameters of our model for the current period are continually exhausting. So it’s hard to get their value right, ”said Agrawal.
Although the scientists know the limitations of such predictions due to variation in data from a large country like India, they cannot stop working on the model because, at least, such predictions provide some basic information for policy makers to finetune their response mechanism.
“Prediction gives you a fair estimate of what you need to do (like hospital bed arrangement, ICUs, medical grade oxygen etc) in the next month. Although there is a risk of going wrong, we cannot stop doing it because such modeling is very important in preparing ourselves for the crisis, ”said Agrawal.