Predicting when the pandemic will end: the role of super-spreaders?

The rolling out of national vaccination programmes on their own will not completely eradicate the virus, because even the most successful vaccines cannot prevent some transmission.  Interest is now focusing on the possible role of ‘super-spreaders’ in maintaining the pandemic.  In this post I review some very recent data on what makes a super-spreader and what influence they might have on the future number of cases.

Apologies but I do have to mention R!

  • As a concept R (more correctly referred to as R0) – the transmissibility of a viral infection – is helpful:  the greater it is, the more quickly an infection will spread 
  • R is the average number of new cases infected from one existing case
  • Here are the R  values for some common infections that, like Covid-19, are transmitted from person to person in the same ways:
  • No surprises perhaps, although interestingly seasonal flu might have an R below 1, which is why even without social distancing a flu epidemic can die out 
  • Note that these are the values in “normal times”, when there are no preventive measures in place (masks/distancing etc), but of course there will be a range of values depending on how much social contact there is between cases
  • The common-sense conclusion is that these R values are helpful in comparing between viruses, but the precise values are wobbly at best

R  and Coronavirus infections

  • In this pandemic there has been a considerable focus on how transmissible the infection is, for good reasons
  • One major reason is that unlike the infections above, there was no prior disease-acquired or vaccine-induced immunity, so the infection could spread at its ‘natural’ rate
  • So what is our estimate of the ‘natural’ value of R?  In this table I have compiled experts’ best estimates and have compared these with estimates for the other major SARS pandemics and also for the most recent variants 
  • Thus Covid-19 is very similar to the other coronaviruses
  • It is interesting, though, to see how small changes in R can have a major influence on the rate of cases
  • In the figure below, as an example, I used the estimate that in the UK, by the time we got round to doing anything serious about controlling the spread, there were 30,000 cases dotted around the country
  • (for non-UK readers the principles are identical!)
  • I have made a series of assumptions about the period of infectivity, but I have calculated the speed of growth/decline for various values of Rfrom a high of 2.5 to a low of 0.7, over a 12 month period
  • The red dotted line is for R of 1, so the number of cases (in this example 30,000) stays the same over the year
For those of you not familiar, the vertical axis is on a logarithmic scale to show the exponential growth 
  • The graph clearly shows that with an R value of 2.5, after 6 months the original 30,000 cases would grow to around 10 million
  • Similarly, with an R value of 0.9, even after 12 months there would still be around 8000 cases: enough to cause problems
  • Getting R to 0.7 though could eliminate the infection (from my calculations!) in 7 months
  • If the R value was indeed  as high as 3.6, or we needed to allow for greater transmissibility of the new variants, then the growth would be even greater

How do super-spreaders change the picture?

  • An R value is an average and would conceal the fact that some people spread to no-one and others to several 
  • There have been some infamous cases, most notably a Santa Claus visit to an old people’s home in Belgium delivered more than they were expecting!
  • This is not an isolated incident but one that, given the circumstances, was easy to track down
  • How much do super-spreaders contribute to the overall number of cases in a population?
  • There has been an interesting analysis from China, which charted the paths by which individual cases in 8 areas caught the infection
  • This is best illustrated in the figure below:
    • In very simple terms, the dots with no lines show cases with no spread, whereas there are some cases who pass on to several others (eg shown by the red arrow)
    • The bar graph shows what proportion of cases passed the infection on to one or more others
    • 80% of cases spread to nobody else
    • Thus just 20% of cases are responsible for the spread of the virus 
    • This highlights the need to consider the role of super-spreaders 

What makes a super-spreader?

  • One question is whether it is there are differences in the virus in people who are super-spreaders
  • The short answer is – very unlikely
  • For sure some new variants show greater powers of transmissibility (see Table above) but:
    • There are super-spreaders with none of the variants
    • The increase in R is modest (less than 1.0)
    • The thought is that people infected with these variants transmit more virus because the mutations allow the virus to replicate more easily and thus infected people breathe out more virus
  • Thus, the answer to ‘why super-spreaders’ must lie within the person.  What are the options?
  • Super-spreaders have more social contacts
    • This is unlikely and did not explain the super-spreading events which had been traced to specific individuals 
  • Super-spreaders generally breathe out more stuff
    • Unlikely as it sounds, this actually may be true
    • We know from experiments that different activities such as singing, shouting etc are associated with breathing out more virus in those who are infected
    • What appears to be the case is that there are some individuals who normally (ie when not infected) breathe out more droplets (which in an infected person could contain the virus)
    • Look at this data from a non-per-reviewed publication of 2 weeks ago
    • In this study 74 volunteers had their breath examined for the number of droplets they breathed out
Each line represents one person: so the biggest exhaler breathed out over 3000 droplets, with many others breathing out less than 10 that barely register on this graph
  • This shows dramatically that there are 1000-fold differences between normal people in the amount of droplets that they breathe out
  • Is there anything different about people who breathe out more droplets?
    • The same study showed that older people breathe out much more than younger people
    • (This might explain the lower transmissibility in children)
    • People who are more obese breathe out more droplets than leaner folk
    • Adding age and obesity together, as these researchers in the USA did, showed that indeed super-spreaders 
      • did breathe out more droplets and
      • were much more likely to be older and fatter
    • This is illustrated in the graph below 


  • Even with the current reduction in transmissibility (as a consequence of the lockdowns in different countries), the infection will still take several months to essentially stop being a public health problem 
  • Super-spreaders could make an important dent in the success rate
  • These preliminary results suggest that it would be helpful for efforts encouraging mask-wearing to focus on those most at risk of exhaling large amounts of droplets
  • It would be a shame to constrain the obvious success of the vaccines in substantially reducing the severity of Covid-19 by not considering this issue

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2 replies on “Predicting when the pandemic will end: the role of super-spreaders?”

I have a number of questions:
1. Why did SARS and MERS not become pandemics as their R rate is so much higher than Covid 19?
2. There is quite a lot of evidence now that Covid 19 was in this country from November 2019, or earlier – not least from my neighbour, whose son got Covid at the end of 2019. Why did it suddenly take off? Could it be your super-spreaders suddenly got it?
3. The White House super spreader event seemed to stop after a short period with no masks being worn, and precious little distancing. No-one died. Was this just sheer luck?


Great questions!
1. 2 theories about SARS and MERS: the case fatality rates were very high and thus people got too ill before they could spread too much, second is that the virus mutated (unlike CoVid19) to much less virulent form and died out. The quoted R values were at its peak

2. And almost certainly from recent data in France and Italy in late 2019. Took off because (i) no-one had immunity and (ii) longish and quite common asymptomatic phase from early infectees

3. Luck probably about the low mortality, but the second attack rate often lower


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