“This Overlooked Variable Is the Key to the Pandemic”
Zeynep Tufekci says that we are paying too much attention to the R value of SARS-CoV-2 (basically the measure of its contagiousness) and not nearly enough attention to the k value (“whether a virus spreads in a steady manner or in big bursts, whereby one person infects many, all at once”).
There are COVID-19 incidents in which a single person likely infected 80 percent or more of the people in the room in just a few hours. But, at other times, COVID-19 can be surprisingly much less contagious. Overdispersion and super-spreading of this virus is found in research across the globe. A growing number of studies estimate that a majority of infected people may not infect a single other person. A recent paper found that in Hong Kong, which had extensive testing and contact tracing, about 19 percent of cases were responsible for 80 percent of transmission, while 69 percent of cases did not infect another person. This finding is not rare: Multiple studies from the beginning have suggested that as few as 10 to 20 percent of infected people may be responsible for as much as 80 to 90 percent of transmission, and that many people barely transmit it.
We’ve known, or at least suspected, this about SARS-CoV-2 for awhile now — I linked to two articles about superspreading back in May and June — but Tufekci says we have not adjusted our thinking about what that means for prevention. We should be avoiding superspreading environments/events (“Avoid Crowding, Indoors, low Ventilation, Close proximity, long Duration, Unmasked, Talking/singing/Yelling”), doing backwards contact tracing, and rapid testing.
In an overdispersed regime, identifying transmission events (someone infected someone else) is more important than identifying infected individuals. Consider an infected person and their 20 forward contacts-people they met since they got infected. Let’s say we test 10 of them with a cheap, rapid test and get our results back in an hour or two. This isn’t a great way to determine exactly who is sick out of that 10, because our test will miss some positives, but that’s fine for our purposes. If everyone is negative, we can act as if nobody is infected, because the test is pretty good at finding negatives. However, the moment we find a few transmissions, we know we may have a super-spreader event, and we can tell all 20 people to assume they are positive and to self-isolate-if there is one or two transmissions, it’s likely there’s more exactly because of the clustering behavior. Depending on age and other factors, we can test those people individually using PCR tests, which can pinpoint who is infected, or ask them all to wait it out.
Part of the problem is that dispersion and its effects aren’t all that intuitive.
Overdispersion makes it harder for us to absorb lessons from the world because it interferes with how we ordinarily think about cause and effect. For example, it means that events that result in spreading and non-spreading of the virus are asymmetric in their ability to inform us. Take the highly publicized case in Springfield, Missouri, in which two infected hairstylists, both of whom wore masks, continued to work with clients while symptomatic. It turns out that no apparent infections were found among the 139 exposed clients (67 were directly tested; the rest did not report getting sick). While there is a lot of evidence that masks are crucial in dampening transmission, that event alone wouldn’t tell us if masks work. In contrast, studying transmission, the rarer event, can be quite informative. Had those two hairstylists transmitted the virus to large numbers of people despite everyone wearing masks, it would be important evidence that, perhaps, masks aren’t useful in preventing super-spreading.
The piece is an important read and interesting throughout: just read the whole thing.
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