Latest estimate (12 June) for the South West region is thought to lie between 0.8 to 1.1. This is a large range which Owl doesn’t find very informative because it suggests that infection rates could be either shrinking or expanding!
Owl is also somewhat confused by the explanation of how R is calculated or rather inferred. After being blinded by the science Owl is left with the distinct impression that there is a degree of circularity in arriving at R. It seems that the self same epidemial models being used to inform decisions are run in reverse so as to infer what R value would be needed as input into the models to “predict” the current levels of infection.
There remain some key known unknowns which are likely to have a big influence on the dynamics of how the infection progresses. There is evidence that a substantial proportion of the population has had an asymptomatic infection but we don’t know whether asymptomatic individuals can infect others. Recently at Weston hospital asymptomatic staff tested up to 40% positive. Antibody testing (Boris Johnson’s game changer) doesn’t seem to be coming out with high enough numbers to indicate the build up of herd immunity, but it isn’t the only indicator of immunity.
And the level of false negative results of testing seems to be of the order of 30% which looks rather high.
Meanwhile the Covid-19 symptom study estimated on 10 June that infection rates are down 39% on the week before in England based on data from 24 May to 6 June, implying an overall R much less than 1 where the Sage range is 0.8 to 1.0.
Locally, the four seaside districts in Devon Owl is keeping an eye on, are all showing 0.4% symptom rates. On 5 June when the announcement was first made that the R value was 1 in the South West, East Devon stood at 0.3%, Torbay at 0.4% and North Devon and the South Hams were at 0.2%. So symptom rates are not declining, maybe starting to rise, maybe statistical noise, but not back to the 13 May levels when Boris announced unrestricted travel to beauty spots. These need watching as further relaxations of lockdown are implemented.
Finally there is also the K number to consider
Latest R number range for the UK
Last updated on Friday 12 June 2020.
What is R?
The reproduction number (R) is the average number of secondary infections produced by 1 infected person.
An R number of 1 means that on average every person who is infected will infect 1 other person, meaning the total number of new infections is stable. If R is 2, on average, each infected person infects 2 more people. If R is 0.5 then on average for each 2 infected people, there will be only 1 new infection. If R is greater than 1 the epidemic is growing, if R is less than 1 the epidemic is shrinking.
R can change over time. For example, it falls when there is a reduction in the number of contacts between people, which reduces transmission. It is not an exact number, it is a calculated estimate.
R is not the only important measure of the epidemic. R indicates whether overall the epidemic is trending towards getting bigger or smaller but not how large it is. The number of people currently infected with coronavirus (COVID-19) – and so able to pass it on – is very important.
R should always be considered alongside the number of people currently infected. If R equals 1 with 100,000 people currently infected, it is a very different situation to R equals 1 with 1,000 people currently infected.
How R is estimated
Individual modelling groups use a range of data to estimate R including:
- epidemiological data such as hospital admissions, ICU admissions and deaths – it generally takes 2 to 3 weeks for changes in R to be reflected in these data sources, due to the time between infection and needing hospital care
- contact pattern surveys that gather information on behaviour – these can be quicker (with a lag of around a week) but can be open to bias as they often rely on self-reported behaviour
- household infection surveys where swabs are performed on individuals which can provide estimates of how many people are infected – longitudinal surveys (which sample the same people repeatedly) allow a more direct estimate of the growth in infection rates
Different modelling groups use different data sources to estimate R using mathematical models that simulate the spread of infections. Some may even use all these sources of information to adjust their models to better reflect the real-world situation. But there is uncertainty in all these data sources, which is why R estimates can vary between different models, and why we do not rely on one model; evidence from several models is considered, discussed, combined, and R is presented as a range. The most likely true value is in the middle of this range.
Who estimates R?
R is estimated by a range of independent modelling groups based in universities and Public Health England (PHE). The modelling groups discuss their individual R estimates at the Science Pandemic Influenza Modelling group (SPI-M) – a subgroup of SAGE. Attendees compare the different estimates of R and SPI-M collectively agrees a range which R is very likely to be within.
Limitations of R
R is an average value that can vary in different parts of the country, communities, and subsections of the population. It cannot be measured directly so there is always some uncertainty around its exact value. This becomes even more of a problem when calculating R using small numbers of cases, either due to lower infection rates or smaller geographical areas. This may be due to the uncertainty and variability in the underlying data and can lead to a wider range for R and more frequent changes in the estimates.
Even when the overall UK R estimate is below 1, some regions may have R estimates that include ranges that exceed 1, for example from 0.7 to 1.1; this does not necessarily mean the epidemic regionally is increasing, just that the uncertainty in the data means it cannot be ruled out. It is also possible that an outbreak in one place could result in an R above 1 for the whole region.
Estimates of R for geographies smaller than regional level are less reliable and it is more appropriate to identify local hotspots through, for example, monitoring numbers of cases, hospitalisations, and deaths.
More useful measures as the epidemic shrinks will be the growth rate, and measures of incidence and prevalence. We hope to be providing these numbers over the coming weeks.
Latest R number ranges for NHS England Regions
These are the latest R estimates by NHS England regions. R values are shown as the range, and the most likely estimate is in the middle of this range.
|East of England||0.7-0.9|
|North East and Yorkshire||0.7-1.0|
Latest R number ranges for devolved administrations
The latest ranges for R in the devolved administrations are published on their respective websites. The values can be found with the links below.
Northern Ireland: Link to Northern Ireland reproduction number
Scotland: Link to Scotland reproduction number
R does not give us insight as to how quickly an epidemic is changing, for instance, different diseases can spread at different speeds. It may take one infected individual with one disease years to infect two people (R=2), whereas someone infected with a different disease may take hours to infect two people (R=2).
The growth rate reflects how quickly the number of infections are changing day by day. If the growth rate is greater than zero (i.e. positive), then the disease will grow. If the growth rate is less than zero (i.e. negative) then the disease will shrink.
The size of the growth rate indicates the speed of change. A growth rate of +5% will grow faster than one with a growth rate of +1%. Likewise, a disease with a growth rate of -4% will be shrinking much faster than a disease with growth rate of -0.5%. Further technical information on growth rate can be found in the article, The growth rate of Covid-19.
The growth rate requires fewer assumptions about the disease when it is calculated.
Neither measure, R or growth rate, is “better” but each provide information that is useful in monitoring the spread of a disease. As the epidemic progresses and numbers of cases decrease, R becomes a less helpful indicator and other measures need to be considered. These include the number of new cases of the disease identified during a specified time period (incidence), and the proportion of the population with the disease at a given point in time (prevalence), and these will become more important.
In the future, SAGE will move away from publishing R estimates as they become less informative and move towards publishing more appropriate measures. From next week, growth rates will be published weekly. At a later date, additional metrics will provide estimates of incidence and prevalence, such as those from the ONS COVID-19 infection survey.
Published 15 May 2020
Last updated 12 June 2020 + show all updates
- 12 June 2020
The R number range for the UK is 0.7-0.9 as of 12 June 2020.
- 5 June 2020
The R number range for the UK is 0.7-0.9 as of 5 June 2020.
- 29 May 2020
The R number range for the UK is 0.7-0.9 as of 29 May 2020.
- 22 May 2020
The R number range for the UK is 0.7-1.0 as of 22 May 2020.
- 15 May 2020