This is long, dense, and excellent article:
https://www.theatlantic.com/health/archive/2020/09/k-overlooked-variable-driving-pandemic/616548/

Article author: “Zeynep Tufekci is a contributing writer at The Atlantic and an associate professor at the University of North Carolina. She studies the interaction between digital technology, artificial intelligence, and society.”

Here is my executive summary with a few observations of my own.

Coronavirus spreads not linearly/deterministically like the flu, but stochastically (called “overdispersion” in the article). 20% of infected people cause almost all the spread and they do most of the spreading at super-spreader events. This makes it much harder to predict. Thus, randomness plays a much larger role — what matters is whether you happen to attend a super-spreader event with one of the 20% who are near peak infectiousness (one such person can infect a hundred people).

The countries that have done well have minimized the super-spreader events. Things like restricting border crossings are not nearly as important as they are for linear diseases like influenza. Avoiding super-spreaders is most important.

Japan did well not because they are an island, but because they realized early on that they needed to focus on the overdispersion impact. Hitoshi Oshitani of Japan’s Covid Task Force “likens his country’s approach to looking at a forest and trying to find the clusters, not the trees. Meanwhile, he believes, the Western world was getting distracted by the trees, and got lost among them.” And thus missed clusters.

This article tells us how to use this knowledge, beyond the obvious things like banning large closeup gatherings that are necessary for super-spreading. Some points I extracted:

1 — Identifying transmission events is more important than identifying infected individuals. Testing plans that use lots of cheap, fast, inaccurate tests can be very effective at finding these events.

For example, if you test 20 people who were at an event with a test that detects only 70% of infections, you will know with high certainty that it was not a super spreader if all 20 are negative (even though there may be some false negatives), and also that it is likely a super spreader if 5 or more are positive (or at least warrants further investigation).

2 — “Overdispersion should also inform our contact-tracing efforts.” This means contract tracing is much more effective looking back in time to figure out what super spreader infected the person, which will help identify many infections. Looking forward in time to trace contacts won’t help as much unless that person happens to become a super spreader.

DEW comment (not in article): In Trump’s case, tracing back to the Rose Garden (or whatever the super spreader was) and then going forward from there would be a much better use of resources than only tracing Trump’s contacts since diagnosis. Of course, if you have the resources to do both, definitely do both. But it appears the White House is not cooperating with either tracing effort (e.g., they refused contract tracing by the CDC and will not disclose the time of his latest negative test) so our country will get neither.

South Korea concentrated on backward tracing and has one of the best responses.

3 — “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.”

Nevertheless, “we can study failures to understand which conditions turn bad luck into catastrophes.”

4– The Good news: Once we understand that eliminating clusters is the key, we can ease up many of our restrictions that are aimed more at linear transmission. “It’s not always the restrictiveness of the rules, but whether they target the right dangers.”

Closing summary (everything selectively quoted from the article, italics and bold by DEW)

“It’s not intellectually satisfying, but because of the overdispersion and its stochasticity, there may not be an explanation beyond that the worst-hit regions, at least initially, simply had a few unlucky early super-spreading events. …

Once we recognize super-spreading as a key lever, countries that look as if they were too relaxed in some aspects appear very different, and our usual polarized debates about the pandemic are scrambled, too.

It’s not that Japan was better situated than the United States in the beginning. Similar to the U.S. and Europe, Oshitani told me, Japan did not initially have the PCR capacity to do widespread testing. Nor could it [legally] impose a full lockdown or strict stay-at-home orders …

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’s commitment to ‘cluster-busting’ allowed it to achieve impressive mitigation with judiciously chosen restrictions. Countries that have ignored super-spreading have risked getting the worst of both worlds: burdensome restrictions that fail to achieve substantial mitigation. “