The SARS-CoV-2 virus has a reproduction number of three, indicating that on average it will infect three other individuals over the course of the infection. However, a study at the Massachusetts Institute of Technology (MIT) found that extreme “super-spreader” events, such as the one that happened at a White House event in September, seem to generate more infections than would otherwise be expected due to random distributions.
“Super-spreading events are likely more important than most of us had initially realized. Even though they are extreme events, they are probable and thus are likely occurring at a higher frequency than we thought. If we can control the super-spreading events, we have a much greater chance of getting this pandemic under control,” said James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, and the senior author of this study.
The researchers analysed a number of documented “super-spreader events” that have taken place over the COVID pandemic. When they ran statistical analyses on super-spreader events, they found that instead of the expected “bell curve” of normal distribution, they found a “fat tail” of extreme events.
Lead author, MIT postdoc Felix Wong said, “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.”
Source: Medical Xpress
