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While studying around 60 superpropagation events in which one COVID-19 patient infected more than six people, the researchers noted that these events were much more common compared to what is observed through statistical distributions used in epidemiology. .
The structure and cross-sectional view of the human coronavirus. It shows the shape of the coronavirus, as well as the cross-sectional view. The image shows the main elements, including the Spike S protein, the HE protein, the viral envelope, and the helical RNA. Image Credit: Wikipedia
So far, more than 47 million people have tested positive for COVID-19 infection in the world. Of these, more than 34 million have recovered and more than 1.2 million have died.
The virus is spread through droplet transmission when an infected person coughs, talks, or sneezes. Based on current data, the basic reproduction number of COVID-19 is approximately three. This means that an infected person can infect three people on average. However, the transmission of this disease is not so simple.
Many people do not transmit the disease at all. On the other hand, several super-spread events have been reported during the pandemic in which it was observed that an infected person infected more than three healthy people. Still, scientists didn’t know exactly what role superpropagation events play in the overall spread of the disease.
Now, a group of MIT researchers claim that superpropagation events have a much greater impact on the transmission of SARS-CoV-2 than was assumed or expected.
The study findings are published in the Proceedings of the National Academy of Sciences of the United States of America.
By studying around 60 superpropagation events in which a COVID-19 patient infected more than six people, the researchers noted that these events were much more common compared to what is observed through statistical distributions used in epidemiology. .
So, to study the scope of super-propagation events and understand ways to control the outbreak, the researchers used mathematical tools used in finance and insurance models to study thick-tailed events (those with a tendency to have extreme deviation median) looking at overlapping events in the past.
They defined a super-spread event as one in which an infected person transmitted the disease to more than six people and, based on this, they found a total of 60 super-spread events: 15 from the SARS outbreak in 2003 and 45 from COVID-19 . outbreak. According to an MIT press release, during most of these events, between 10 and 100 people were infected. However, in two events, more than 100 people were infected.
By plotting the findings on a graph, the researchers found that superpropagation events are extreme, but still more common than previously anticipated.
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The study authors noted that several factors such as viral load (the amount of virus present in the body) decide whether a person is a super-spreader. A previous study had also indicated that people who release more droplets when coughing or talking are more likely to transmit COVID-19 to more people.
However, these factors were not included in the last study. Instead, the researchers focused on the number of people an infected person comes into contact with.
To study this, they created and compared two mathematical models. The average number of people who came into contact with each person was 10 in both models. However, in one of the models, the total number of contacts per person continued to decrease exponentially, while some people in the other model had come into contact with a greater number of people.
In the second model, more people were found to be infected. However, the number dropped significantly when people with more than 10 contacts were taken out of context.
The authors mentioned that if we can reduce the total number of people a person comes into contact with to less than 10, super-propagation events can be significantly reduced.
For more information, read our article on Who Are Super Spreads and How To Stop Them.
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