Published: 28th February 2022
WhatTheFAQ: How real is the threat of the fourth wave in India and when can we expect it?
Researchers at IIT Kanpur have been predicting COVID waves in India with near-perfect accuracy. And so, when they say the fourth wave might hit in June, we might as well dig a little deeper
With Delhi lifting the last of COVID restrictions after what was a relatively mild wave of the pandemic, it might seem as if it's time to put those masks and sanitisers back inside our drawers and feel the wind on our faces again. However, a word of sobriety comes from IIT Kanpur, the Indian institution that has been predicting COVID waves in the country with near-perfect accuracy. So, when is the fourth COVID wave expected to hit India and how does the wave prediction system work?
When does IIT Kanpur predict the fourth wave?
The researchers at IIT Kanpur have predicted that the fourth wave might hit India on June 22 and go on up until October 24. That means it will last almost four months.
How severe will this wave be?
Well, severity is one thing that cannot be ascertained at the moment. It depends on the way variants of the virus mutate in the months leading up to the possible wave and the status of vaccination in the population. At the start of this year, 70% of India's adult population was reportedly fully vaccinated. By June, the focus will be on how far the country has progressed in its booster dose numbers.
When is it expected to peak?
The peak period of the wave is expected to be in the last two weeks of August. According to the researchers, August 15 to August 31 is expected to be the period of concern, with the wave reaching its peak on August 23.
What method was used to predict the wave?
The mixture of Gaussian distribution, or the normal distribution method, which arranges variables on a bell graph. This was done by collecting data on previous waves in the country. To determine the peak of the wave, the Bootstrap technique was used. The Bootstrap technique applies measures of accuracy to the sample to estimate the sample distribution.