by Shlomo Maital

    Policy begins with measurement.  If you measure wrongly, you act wrongly.  Result: Fiasco.

    My country Israel is deep in a COVID-19 second wave, far worse than the first, with 7,000 and more new cases daily, for a small country of some 9 million people.  There are nearly 900 people in intensive care – stretching ICU capacity to the limit.  Medical staff, working non-stop since February, are crushed under the burden. And now, a country-wide severe lockdown, closing businesses and schools, closing everything.

    So why is this a fiasco?  It is explained clearly in this article:  “This Overlooked Variable Is the Key to the Pandemic. It’s not R0.”  ZEYNEP TUFEKCI.   SEPTEMBER 30, 2020    The Atlantic. 

    This is a very long blog.  So here is a brief summary.  Political leaders, guided by public health experts, are focused on R0 – this is the AVERAGE number of people infected by one person already infected.  R0 bigger than one:  exponential spread.  R0 less than one:  negative exponential decline.  So the goal is: Get R0 down.

    Here is the problem. R0 is an average.  Misleading.  Why?  Consider a group of two. One is a millionaire.  One is a pauper.  There average income is $500,000.  So – all is well?

    Most people infected with COVID-19 do not spread it.  A few spread it as super-spreaders.  Why not focus on the super-spreaders?  Why shut down everything, just to catch that handful of super-spreaders?   “K” is a statistical measure of “dispersion” or “scatter” – do many people infect many others, or do a very few infect many others?  Because, if it is the latter – then if we can find the super-spreaders, we can halt the spread, without disastrously shutting down the whole world!

   And – how do you find the super-spreaders?   Backward tracing.  Conventionally, we trace forward.  You have COVID-19?  OK – with whom did you associate? And with whom did each of those associate?

    Backward tracing means:  Take a thousand persons infected in a given place.  Work backward, to see who was it and where was it and when was it that they were infected?  With the goal of finding that COVID-19 Mary – that super-spreader!   

     For example:  At the Rose Garden ceremony introducing Judge Amy Barrett – who in the front row infected so many people, including the President, Hope Hicks, the campaign manager, and so many others?   And most important – shut down mass events – like bars, weddings, and Trump rallies – that super-spread virus.

    And now, the whole story.

= = = = =

     The R mentioned in the daily press briefings represents an average of the whole country or region, involving millions of people. But its single value hides many differences between individuals and their impact on virus transmission.

      Rather than assuming that every infected person and every contact they make follows the same pattern (as with the R number), scientists working on epidemic models allow for the number of new cases caused by each infected person to vary randomly.  Some people might have high viral loads or might simply cough more and hence spread the virus more effectively.   Many people, although ill and highly infectious, don’t show any symptoms. They might make many contacts without realising they pose a danger to others. An example from history is the infamous Mary Mallon (“Typhoid Mary”), a cook in New York City in the early 1900s. Although she carried typhoid bacteria, she didn’t show any symptoms and is believed to have infected more than 50 people over seven years.

   Here is what Zeynep Tufekci writes:

  “…. averages aren’t always useful for understanding the distribution of a phenomenon, especially if it has widely varying behavior. If Amazon’s CEO, Jeff Bezos, walks into a bar with 100 regular people in it, the average wealth in that bar suddenly exceeds $1 billion. If I also walk into that bar, not much will change. Clearly, the average is not that useful a number to understand the distribution of wealth in that bar, or how to change it. Sometimes, the mean is not the message. Meanwhile, if the bar has a person infected with COVID-19, and if it is also poorly ventilated and loud, causing people to speak loudly at close range, almost everyone in the room could potentially be infected—a pattern that’s been observed many times since the pandemic begin, and that is similarly not captured by R. That’s where the dispersion comes in.    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.”

     Notice this:  It is the 80/20 rules so well known in management.  20% of the people do 80% of the work.  19% of the cases cause 80% of the transmission.  So the goal has to be not R0  but K:  FIND THOSE 19%!!!  STOP THOSE MASS EVENT WHERE THE 19% ARE SPREADING VIRUS!.

    OK.  Prove it.    So here is Japan’s approach based on “K”.

“Hitoshi Oshitani, a member of the National COVID-19 Cluster Taskforce at Japan’s Ministry of Health, Labour and Welfare and a professor at Tohoku University who told me that Japan focused on the overdispersion impact from early on, 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. To fight a super-spreading disease effectively, policy makers need to figure out why super-spreading happens, and they need to understand how it affects everything, including our contact-tracing methods and our testing regimes.

“In study after study, we see that super-spreading clusters of COVID-19 almost overwhelmingly occur in poorly ventilated, indoor environments where many people congregate over time—weddings, churches, choirs, gyms, funerals, restaurants, and such—especially when there is loud talking or singing without masks.”

   And now – my country Israel.  28% of Ultra-Orthodox tested for coronavirus proved positive.  Their lifestyle focuses on community prayer and celebration.  Some 40% of all new cases come from this community, which is only a small fraction of the population.

    Yet the whole country is locked down by the Prime Minister and cabinet. Why?  To focus on the Ultra-Orthodox would be discriminatory,  racist, anti-Semitic – and worst of all, politically damaging to the Prime Minister, who needs their votes and support, in his coalition.

     So Israel’s “K” is small.  We have superspreaders. But because of politics, we manage our policies based on R0.     When Israel’s Project Manager for COVID-19 control, Dr. Roni Gamzu, mentioned the Ultra-Orthodox as super-spreaders, based on medical evidence, he was violently attacked and forced to apologize.


   So let us give  Tufekci the last word – a word of hope.  “Could we get back to a much more normal life by focusing on limiting the conditions for super-spreading events, aggressively engaging in cluster-busting, and deploying cheap, rapid mass tests—that is, once we get our case numbers down to low enough numbers to carry out such a strategy? (Many places with low community transmission could start immediately.) Once we look for and see the forest, it becomes easier to find our way out.”