That’s why in addition to R, scientists use a value called the dispersion factor (k), which describes how much a disease clusters. The lower k is, the more transmission comes from a small number of people. In a seminal 2005 Nature paper, Lloyd-Smith and co-authors estimated that SARS—in which superspreading played a major role—had a k of 0.16. The estimated k for MERS, which emerged in 2012, is about 0.25. In the flu pandemic of 1918, in contrast, the value was about one, indicating that clusters played less of a role.
Estimates of k for SARS-CoV-2 vary. In January, Julien Riou and Christian Althaus at the University of Bern simulated the epidemic in China for different combinations of R and k and compared the outcomes with what had actually taken place. They concluded that k for COVID-19 is somewhat higher than for SARS and MERS. That seems about right, says Gabriel Leung, a modeler at the University of Hong Kong. “I don’t think this is quite like SARS or MERS, where we observed very large superspreading clusters”, Leung says. “But we are certainly seeing a lot of concentrated clusters where a small proportion of people are responsible for a large proportion of infections.” But in a recent preprint, Adam Kucharski of LSHTM estimated that k for COVID-19 is as low as 0.1. “Probably about 10% of cases lead to 80% of the spread”, Kucharski says.
That could explain some puzzling aspects of this pandemic, including why the virus did not take off around the world sooner after it emerged in China, and why some very early cases elsewhere—such as one in France in late December 2019, reported on 3 May—apparently failed to ignite a wider outbreak. If k is really 0.1, then most chains of infection die out by themselves and SARS-CoV-2 needs to be introduced undetected into a new country at least four times to have an even chance of establishing itself, Kucharski says. If the Chinese epidemic was a big fire that sent sparks flying around the world, most of the sparks simply fizzled out.
Kai Kupferschmidt
That is an interesting avenue of research, which supports other recommendations for avoiding large groups of people, particularly indoors, and at the same time offers plausible explanations for some of the strange spreading patterns of this virus. As I noticed in my first article about the pandemic, there are significant differences in the initial patterns of outbreak between countries: some see exponential growth very early after the first reported cases, while others remain around a dozen cases for up to a month. In Romania as well superspreading was a big factor; official reports from March mention that 63% of localized spreading was traced back to just four people – the one in the capital ended up infecting 47 others!
In the light of this analysis, I must wonder how many of the forecasting models passed around in the press and used to make high-level policy decisions take this dispersion factor into account… The inherent variability of viral spreading would make long-term predictions highly susceptible to small changes in case numbers and conditions; a lone traveler with no symptoms could trigger new outbreaks seemingly out of nowhere. All the more reason to stay cautious and not relax too much, too soon.
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