From choir rehearsals in Washington to family reunions in Chicago, many covid-19 “super-diffusion” events have seen one person infect many more. MIT researchers who studied about 60 of these events found that they had a much larger impact than expected.
“Big-air events are probably more important than most of us initially realized,” says lead author James Collins, professor of medical engineering and science. If we can control them, he adds, “we have a much better chance of bringing this pandemic under control”.
For SARS-CoV-2, the “base reproduction number” is around 3, which means that on average, each infected person will pass it on to around three others. But some don’t pass the disease on to anyone, while “super-spreaders” can infect dozens. By defining super-spreaders as those who transmitted the virus to more than six others, Collins and postdoctoral fellow Felix Wong identified 45 wide spread events from the current SARS-CoV-2 pandemic and 15 events from the outbreak of SARS-CoV from 2003, all documented in scientific journals. During most of these events, between 10 and 55 people were infected, but two in 2003 involved more than 100 people.
Considering the commonly used statistical distributions in which the typical patient infects three others, events in which the disease spreads to dozens of people would be considered highly unlikely. A normal distribution would look like a bell curve with a peak around three and a tail that tapers rapidly in both directions, meaning the likelihood of an extreme event decreases exponentially as the number of infections increases. deviates from the mean.
But by applying mathematical tools often used in the finance and insurance industries to model extreme events, the researchers found that the distribution of coronavirus transmissions has a fat tail rather than a tapering reduction, implying that even though mass market events are extreme, they are still probable. arrive.
While there are many factors that can contribute to making a person a super-spreader, researchers have focused on the number of people an infected person comes in contact with. They created and compared two network models, both with an average of 10 contacts per person. But one had an exponential declining contact distribution, while the other had a big tail in which some people had many contacts. In this model, many more people were infected by mass-market events. However, the transmission stopped when people with more than 10 contacts were removed from the network.
The results suggest that capping gatherings at 10 could significantly reduce the number of wide spread events and lower the overall number of infections, the researchers said. —Anne Trafton