There have been many documented cases of COVID-19 “super-spreading” events, in which one person infected with the SARS-CoV-2 virus infects many other people.
But how much of a role do these events play in the overall spread of the disease?
In a new study, researchers found that they have a much larger impact than expected.
They examined 60 super-spreading events and found that events where one person infects more than six other people are much more common than would be expected if the range of transmission rates followed statistical distributions commonly used in epidemiology.
They also developed a mathematical model of COVID-19 transmission, which they used to show that limiting gatherings to 10 or fewer people could strongly reduce the number of super-spreading events and lower the overall number of infections.
The research was conducted by a team from MIT.
For the SARS-CoV-2 virus, the “basic reproduction number” is around 3, meaning that on average, each person infected with the virus will spread it to about three other people.
However, this number varies widely from person to person.
Some people don’t spread the disease to anyone else, while “super-spreaders” can infect dozens of people. Wong and Collins set out to analyze the statistics of these super-spreading events.
In the study, the team defined super-spreaders as individuals who passed the virus to more than six other people.
Using this definition, they identified 45 super-spreading events from the current SARS-CoV-2 pandemic and 15 additional events from the 2003 SARS-CoV outbreak, all documented in scientific journal articles.
During most of these events, between 10 and 55 people were infected, but two of them, both from the 2003 outbreak, involved more than 100 people.
Given 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 very unlikely.
For instance, a normal distribution would resemble a bell jar with a peak around three, with a rapidly-tapering tail in both directions.
In this scenario, the probability of an extreme event declines exponentially as the number of infections moves farther from the average of three.
However, the MIT team found that this was not the case for coronavirus super-spreading events.
To perform their analysis, the researchers used mathematical tools from the field of extreme value theory, which is used to quantify the risk of so-called “fat-tail” events.
Extreme value theory is used to model situations in which extreme events form a large tail instead of a tapering tail.
This theory is often applied in fields such as finance and insurance to model the risk of extreme events, and it is also used to model the frequency of catastrophic weather events such as tornadoes.
Using these mathematical tools, the researchers found that the distribution of coronavirus transmissions has a large tail, implying that even though super-spreading events are extreme, they are still likely to occur.
This means that the probability of extreme events decays more slowly than one would have expected.
These really large super-spreading events, with between 10 and 100 people infected, are much more common than we had anticipated.
Many factors may contribute to making someone a super-spreader, including their viral load and other biological factors.
The findings suggest that preventing super-spreading events could have a strong impact on the overall transmission of COVID-19.
One way to do that would be to prevent anyone from interacting with over 10 people at a large gathering, the team says.
They now hope to study how biological factors might also contribute to super-spreading.
One author of the study is James Collins, the Termeer Professor of Medical Engineering and Science.
The study is published in PNAS.
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