There is no data, either from India or globally, to show that children will be seriously infected in any subsequent Covid-19 waves, AIIMS, Delhi, Director Dr Randeep Guleria said on Tuesday, 8 June.
Addressing a media briefing here, he said that "it is a piece of misinformation that subsequent waves of the Covid-19 pandemic are going to cause severe illness in children".
Guleria futher asserted that "Covid appropriate behaviour is key to preventing future waves", similar to the statement NITI Aayog Member (Health) V.K. Paul made on Monday, 7 June.
Explaining why pandemics return again, the AIIMS Director said "waves normally occur in pandemics caused due to respiratory viruses; the 1918 Spanish Flu, H1N1 (swine) flu are examples".
"The second wave of 1918 Spanish Flu was the biggest, after which there was a smaller third wave," said Guleria. "And as we know, SARS-Cov-2 is a respiratory virus."
Multiple waves occur when there is a susceptible population and when a large part of the population acquires immunity against the infection, "the virus becomes endemic and infection becomes seasonal like that of H1N1 that commonly spreads during monsoon or winters", he said.
Guleria said that one of the reasons behind a wave can be human behaviour and cautioned that "whenever cases increase, there is a fear in people and human behaviour changes. People strictly follow Covid appropriate behaviours and non-pharmaceutical interventions help break the chain of transmission. But when unlocking resumes, people tend to think that not much infection will happen and tend to not follow Covid appropriate behaviour".
Due to this, the AIIMS chief said, the virus again starts spreading in the community, leading potentially to another wave.
"If we have to stop subsequent waves, we need to aggressively follow Covid appropriate behaviour until we can say that a significant number of our population is vaccinated or has acquired natural immunity.”
(This story was published from a syndicated feed. Only the headline and picture has been edited by FIT)
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