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There have been many documented cases of COVID-19 “super spread” events in which one person infected with the SARS-CoV-2 virus infects many other people. But what role do these events play in the general spread of the disease? A new MIT study suggests they have a much greater impact than expected.
Examining approximately 60 super spread events shows that events in which one person infects more than six other people occur much more frequently than expected when the transmission rate range follows the statistical distributions commonly used in epidemiology.
Based on their findings, the researchers also developed a mathematical model of COVID-19 transmission, which they used to show that limiting meetings to 10 or fewer people can significantly reduce the number of super-prevalence events and lower the overall number. of infections.
“Super diffusion events are probably more important than most of us initially thought. Although these are extreme events, they are likely and therefore likely to occur more often than we thought. If we can control super spread events, we have a much better chance of keeping this pandemic under control, “says James Collins, Termeer Professor of Medical Technology and Science at MIT Institute for Medical Technology and Science (IMES) and in the Biological Engineering and senior author of the new study.
MIT postdoc Felix Wong is the lead author of the article appearing in the Proceedings of the National Academy of Sciences this week.
Extreme events
For the SARS-CoV-2 virus, the “base reproduction number” is about 3, which means that, on average, each person infected with the virus passes it on to about three other people. However, this number varies greatly from person to person. Some people don’t spread the disease to others, while “super spreaders” can infect scores of people. Wong and Collins set out to analyze the statistics of these popular events.
“We found that analysis based on the analysis of high prevalence events and past events can shed light on how we should propose strategies to address the outbreak and better control the outbreak,” says Wong.
The researchers defined the super spreaders as people who passed the virus to more than six other people. Using this definition, they identified 45 super spread events from the current SARS-CoV-2 pandemic and 15 additional events from the SARS-CoV outbreak in 2003, all documented in scientific journal articles. Between 10 and 55 people were infected during most of these events, but two of them, both from the 2003 outbreak, involved more than 100 people.
Given the widely used statistical distributions in which the typical patient infects three others, events that would spread the disease to dozens of people are considered very unlikely. For example, a normal distribution would resemble a glass bell with a tip around three, with a tail that tapers quickly in both directions. In this scenario, the probability of an extreme event decreases exponentially as the number of infections moves away from the average of three.
However, the MIT team found that this was not the case with the coronavirus super spread events. For their analysis, the researchers used mathematical tools from the field of extreme value theory, with which the risk of so-called “fat tail” events is quantified. Extreme value theory is used to model situations where extreme events form a large tail instead of a tapered tail. This theory is widely used in areas such as finance and insurance to model the risk of extreme events, and 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, meaning super-diffusion events are still likely to occur, albeit extreme ones.
“This means that the likelihood of extreme events decreases slower than expected,” says Wong. “These really large super spread events with 10 to 100 infected people are much more common than expected.”
Stop Den Spread
Many factors can help make someone a super spreader, including their viral load and other biological factors. The researchers did not address these issues in this study, but they modeled the role of connectivity, defined as the number of people an infected person comes into contact with.
To study the impact of connectivity, the researchers created and compared two mathematical network models for disease transmission. In each model, the average number of contacts per person was 10. However, they designed a model with an exponentially decreasing contact distribution, while the other model had a fat tail where some people had a lot of contacts. In this model, far more people were infected with superspreader events. Transmission stopped
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