If you remove superspreaders from an environment, then herd immunity doesn't have to be as high as 60%."Then you need a lower percentage because you're removing the superspreaders from the beginning and because of that, there is less infection," said Yaron Oz, Rector at Tel Aviv University in Israel. Oz recently published his research on the superspreaders and the role they play in spreading COVID-19.
A few individuals have a larger capacity to infect people with the SARS-CoV-2 virus, who are known as superspreaders. In an interview to Databaaz, Oz said the functioning of superspreaders can be explained on the basis of the Pareto principle which show that for some events, 80% of the effects might come from 20% of the causes.
"I can illustrate this by showing you how 20% of people own about 80% of the money in the world. 10-20% have citations in 80% of research papers. So, this is a phenomenon of all the systems. It's the same thing that is happening here in pandemics, about 10% of the people, sometimes people would say 5%; there are all kinds of details, but they are responsible for about 80 to 90% of the infections. So they are called superspreaders,"said Oz.
They are not only superspreaders in the sense that they infect more, but they are also more susceptible to infection. So, these are the ones who are getting infected very quickly and they are also infecting others.
This can also depend on two factors, biological and social. The biological factor of what makes one a superspreader still needs more research, said Oz.
"There are other issues that are related to biology. Some people tend to cough more, sneeze more. There are issues related to hygiene, some people keep themselves in order, and put on the mask, their chances of infecting other people become less," he added.
The other is social reasons. Professor Oz said, "There are certain people who meet many people during the day. So, their interaction with the environment, with other people is much larger, and they are more susceptible to being infectious, and they're also infecting more."
According to Professor Yaron Oz, taking out superspreaders from the environment will reduce the infectiousness of the disease. This is because everyone doesn't spread the disease with the same intensity.
You can watch the interview here.
Below is the full transcript of the interview moderated by Govindraj Ethiraj.
Govindraj Ethiraj: Hello and welcome, a big mystery in understanding how COVID-19 spreads is the role of superspreaders. It's quite clear now that all infected individuals do not spread the disease with the same intensity. So sometimes it's more a factor of luck about who you are exposed to, or not exposed to, or whether you're in the company of a superspreader or not, again, as some research and evidence have shown, so going a step further, is it possible to predict where a superspreader is likely to be and or it could be happening and how do we bring it or our understanding of it into the realm of public health response?
To discuss this I'm joined by Professor Yaron who has written a paper on superspreaders and high variance infectious diseases. Professor Yaron is the rector of Tel Aviv University and he also has completed magna cum laude two BSc degrees, one in electrical engineering and physics, was a postdoctoral fellow at the University of California Berkeley, and he's also worked as a staff member at the Research Institute CERN in Switzerland and he joined the School of Physics and Astronomy at Tel Aviv University in 2001. Professor ion thank you very much for joining us.
So, tell us about your understanding of how superspreaders or superspreading works?
Professor Yaron Oz: So, let me first explain what there are these superspreaders and I will give you examples from other fields to understand something which is called the Pareto principle.
For instance, It is well known that basically 20% of the people own about 80% of the money. It's the same thing that about 10 or 20% of the people own 80% of the citations to papers. So, this is a phenomenon of all the systems. It's the same thing that is happening here in pandemics, about 10% of the people, sometimes people would say 5%; there are all kinds of details, but they are responsible for about 80 to 90% of the infections. So they are called superspreaders.
They are not only superspreaders in the sense that they infect more, but they are also more susceptible to infection. So, these are the ones who are getting infected very quickly and they are also infecting others.
Now, what is the definition of a superspreader? So, normally the way people quantify a disease like a pandemic is by denoting a quantity called 'R naught' (R0) which is the effective production number, basically, production number which basically says you have been infected and now you are getting certificates to infect other people.
How many certificates you get is different for different people. Let's say you get 10 on the average, then that means that everyone will infect on the average 10 people that will be a huge number, this will be the basic reproduction number.
Typically for pandemics like COVID-19 19 people valuated at an estimated value between 3 to 6.
It depends on the people but it's something of that order. It's a big number. But what is important is that at the beginning of the pandemics, all these ones who are very susceptible and very infectious, they are the ones who begin the spread.
And so this number is very large. But these people are then quickly getting removed from the system because at the moment, we believe that they will be not infected. So, therefore, this has a huge effect on the development of the disease. In contrast, if everybody would infect exactly in the same number, like how everybody gets two certificates, then it doesn't matter. You remove these ones, the next ones will again infect the same or at the same rate. So what is happening now is that these guys are being removed and then you effectively start to see the decrease of the pandemic. Now the issue is to calculate how fast it's going to decrease without any vaccination. The next calculation is to understand why some people are superspreaders and others are not?
Govindraj Ethiraj: Yeah, exactly. So, that was my next question. Right.
Professor Yaron Oz: So there are several reasons for this. One point could be just social. There are certain people who meet many people during the day. So, their interaction with the environment, with other people is much larger, and they are more susceptible to being infectious, and they're also infecting more. That's one thing. Something like this, you can control by restricting the number of people that you meet.
There are other issues that are related to biology. Some people tend to cough more to sneeze more, etc. There are issues related to hygiene, some people keep themselves in order, and put on the mask, their chances of infecting is becoming less and less.
So there are biological and also social reasons for this. The biological reasons no one can can really analyse precisely, but it seems like maybe kids behave differently than adults.
But I don't think there is precise data on it. But socially, it's clear. You see more, in fact.
Govindraj Ethiraj: Right. So you've talked about trying to understand the natural evolution of the disease irrespective of measures, including social measures. So when you say measures, do you think that include things like wearing a mask?
Professor Yaron Oz: Yes, so you can analyse the development of the disease without doing anything, there is a pandemic, we are doing nothing. Let's see how it evolves.
You can do something else, you can say no, I'm doing that. But there are all kinds of social distancing and other requirements. This is going to change the infection parameter for every one of us. So every one of us has basically two parameters. One is the ability to infect others and the susceptibility; and these numbers can be reduced. If you take social measures, and you will use these numbers, you can analyse the pandemics, again with these reduced numbers.
Govindraj Ethiraj: What is the conclusion of your paper? I mean in terms of what can we do today in in in as our understanding of the role of superspreaders increases or improves? What is it that we can do today or should be doing today, either from an overall public health point of view or even in smaller, let's say confines like it could be an organisation or a factory.
Professor Yaron Oz: First of all, what are the conclusions from the fact that there are superspreaders, and then we can discuss what you can do. So, there are two conclusions because of this high variance. This is not homogeneous, not everybody has exactly the same set number of certificates. What happens is that a disease can disappear even if R0 (R naught) is bigger than one normally.
Calculations tell you that unless it's dipped below one, this disease will evolve and people think it will evolve exponentially. So the conclusion is not to this will not happen.
Second, in order to ensure the immunity, you don't need you really need 60%.
It's a less number because you're removing the superspreaders from the beginning and because of that, there is less infection.
So, I can give you an example again with this money. Suppose I look at how much money there exists in the world, how many how much people own things, and I start to remove the very rich ones, like Bezos and others. Now the number will reduce very quickly.
So in this sense, healthy immunity can be reached faster than the 60%. Now what can be done? First of all, we need to identify superspreaders and control them. That will control the disease much faster, so even if there are superspreaders you can minimise their effect.
For instance, if I limit the number of people that the person can meet during the day. That's another possibility.
The third is is what we were doing now in his lab, which we actually suggested to the government several months ago, but they didn't accept it at the time. And now they are implementing it, not to have a complete lockdown on everybody. But divide the country in two parts. It's s a way, it's called now traffic light. Each part has a colour. Colour could be red, big a could be orange could be yellow and could be green.
And then you put the restrictions on the different parts according to the behaviour of the disease. And that's a way to control it.
So in this way, you can you're controlling, especially the superspreaders, because they are the one who really make these exponential goals. And then day by day you see what is happens and then you change the measures. And this is actually now being implemented. Yes.
Govindraj Ethiraj: Right. So, so for this to work, obviously that assumes a very, very high intensity of testing. Is that correct?
Professor Yaron Oz: Yes. So if you have a lot of testing, this is very crucial for controlling the pandemics, if you don't have this data, it's like you have no intelligence. So somebody tells you that there is a fire in the forest, but you don't know where it is exactly what is happening. And so you're showering water on all over the forest -- it makes no sense. You need to have exact data. Yeah. So that's what we recommended is well, many, many random tests, not just test data, you know, somebody says, Okay, I'm sick. Please check me.
Govindraj Ethiraj: Right. So but is there any, any insight or understanding on actually identifying superspreaders at this point? Or, or does that only happen in retrospect, you know, when some there is an event, you found those 50 people who are sitting in a restaurant or a club, and then you go back and start testing and you find that it was only one person who triggered it.
Professor Yaron Oz: Right. So I think biologically we don't know yet. I don't think it's not possible to say okay, this guy, let's check him, he is a superspreader we are not there yet. There is no really clear mark that tells us that, right.
But we do know from all these examples of like football games and others, that one of the reasons that this happened was because there were many people accumulating at the same place. So the superspreader could act. So I think because we don't have really a biologically or biological marker at the moment, what I should do is use the social tools to control it.
Govindraj Ethiraj: Right and speaking about that, you know, it's obviously in a way encouraging to know that maybe only 5% or 10% will actually spread and therefore, the probability of my being exposed to a superspreader is much less, but could that also make us you know, in a way, you know, I mean, not focus on the right amount of prevention and make us lazy, you know, in a way?
Professor Yaron Oz: So, even if there is a distribution of this, this number of certificates, let's say 10 or I give you 10 certificates to infect others. If I take all these measures, I reduce these numbers for everybody. So I reduced for the superspreader, let's say from 10 to six and from the other one from two to one. So, in this sense, these are good measures, because they are they have an effect on everybody and especially also on the superspreader. So, I think we should not be lazy on that that is clear.
Govindraj Ethiraj: Right, you've also published a paper on herd immunity as my last question. So, how are you linking the two now, that I mean you are you reducing the number you already you refer to it a little while ago, it may not be 60% it may be lower. Any other mathematical insights into where that magical herd immunity percentage could be?
Professor Yaron Oz: So the 60% was because people assume that everybody has exactly the same number of certificates to infect others. But this is not the case. And there is a distribution. The result depends on that distribution, how many certificates people have. And it can vary between 5% to 10% to 60%. And now the range is very large, and it depends on the details of the pandemics, but it's definitely not 60%, it is significantly lower than that.
But at the moment, there is not enough data to tell you whether it's 5 or 10 %. Now people say okay, but there are certain cities where we think already 10% have been infected. So have we reached herd immunity?
The problem is, this is an open system. I mean, you don't close the city with this 10% in fact that in reaching her immunity, you're still allowed flocks of people from other places, and this is the difficulty.
But I think it's a good thing to know that we don't really need 60%, and we need much less. And so it is not impossible that we will reach herd immunity before we have a reasonable vaccination.
Govindraj Ethiraj: And when you say much less, is there a figure?
Professor Yaron Oz: Yes, our numbers depend on the distribution. For instance, there is a very well known distribution known as gamma distribution that people use to moderate these pandemics for that one is between 5% to 10%.
Govindraj Ethiraj: You're saying that at 5% to 10%, that's enough for herd immunity.
Professor Yaron Oz: If this is the right distribution that people have identified, then yes. I am hesitating because I'm not sure they have enough data to claim, but if this was the one, this gamma distribution with these parameters that people sort of try to estimate. This is less than 10%. Yes.
Govindraj Ethiraj: Okay. And I'm sure there will be others who have other views on it, and I'll come back at some point on that. So your research fields, Professor, are high energy, particle physics, quantum field theories and superstrings.
How did you get into understanding or trying to understand the spread of COVID-19 from this?
Professor Yaron Oz: Yes, so what happened was that around February, the government asked us to, to help in building a strategy to deal with the pandemics. So, we took several academicians from different disciplines like a data science physicist, epidemiologist, economist, etc. And we prepare the report.
And I was in charge of the mathematical analysis of what should be a mathematical way to control it.
And this is where we propose this thing of dividing the country into two parts with colours and controlling it mathematically. And since then, we decided that let's think about this issue a little bit more. And so my two collaborators, actually from the computer science department, not from physics. And they, it turns out that this is actually, you know, a physical problem, like one of the physical problems we normally try to solve.
That's how we got into it. So actually turns out, at least in Israel, actually, I think all over the world, many, many different disciplines because this became a very important problem, started to use their expertise in order to try to analyse the pandemics.