K: The overlooked variable that's driving the pandemic

From the article:

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 are 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 can think of disease patterns as leaning deterministic or stochastic: In the former, an outbreak’s distribution is more linear and predictable; in the latter, randomness plays a much larger role and predictions are hard, if not impossible, to make. In deterministic trajectories, we expect what happened yesterday to give us a good sense of what to expect tomorrow. Stochastic phenomena, however, don’t operate like that—the same inputs don’t always produce the same outputs, and things can tip over quickly from one state to the other. As Scarpino told me, “Diseases like the flu are pretty nearly deterministic and R0 (while flawed) paints about the right picture (nearly impossible to stop until there’s a vaccine).” That’s not necessarily the case with super-spreading diseases.

[...]

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 are one or two transmissions, there are likely 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.

[...]

Oshitani told me that in Japan, they had noticed the overdispersion characteristics of COVID-19 as early as February, and thus created a strategy focusing mostly on cluster-busting, which tries to prevent one cluster from igniting another. Oshitani said he believes that “the chain of transmission cannot be sustained without a chain of clusters or a megacluster.” Japan thus carried out a cluster-busting approach, including undertaking aggressive backward tracing to uncover clusters. Japan also focused on ventilation, counseling its population to avoid places where the three C’s come together—crowds in closed spaces in close contact, especially if there’s talking or singing—bringing together the science of overdispersion with the recognition of airborne aerosol transmission, as well as presymptomatic and asymptomatic transmission.

For testing, the idea seems to be that identifying people with COVID-19, by itself, isn't all that useful from a public health perspective, because most people never spread the disease. But if you find a transmission from A->B then you need to be concerned about everyone who had contact with A. Since everyone got it from someone, looking backward is useful and testing a lot of people to find one transmission (when looking forward) is useful.