During the coronavirus (COVID-19) pandemic there has been increasing focus on, and interest in, the reproduction number – R. R is the average number of secondary infections produced by 1 infected person.

OSR’s observation of recent presentations of R is that generally a good job is being made of explaining both the number itself and its implications for the UK and each of the devolved nations. However, there is room for estimates of R to be presented more clearly and explained more meaningfully. Lessons can be learnt from the approach to publication of R by different nations of the UK.

Decision-makers across the UK have made it clear that decisions about how we come out of lockdown and whether or not any restrictions need to be re-introduced in future are informed by the value of R.

The latest estimates of R have become widely quoted by scientists, government officials and the media.
R for the UK is estimated by a range of independent modelling groups based in universities and Public Health England (PHE). Scientific advisers and academic modellers compare different estimates of R from the models and collectively agree a range which R is very likely to be within.

Devolved nations tend to use either those same independent models or one preferred model and apply data about the pandemic in their own countries to arrive at their consensus estimates of R. All devolved nations are publishing or intend to publish estimates for the range of R in their different countries on a regular (most on a weekly) basis. We commend the cooperation taking place between the four nations to bring about a consistent approach to R and where it should be published.

We’ve been impressed that explanations have succeeded in conveying the importance of the R-number and the role the estimates play in advice to ministers. We particularly commend;

The accessibility of the statistics

  • Estimates of R sit within a crowded, and sometimes confusing, landscape of other data and we found that broadly the needs of different types of users and potential users have been taken into account in the presentation and release of the statistics and data.

The presentation of uncertainty

  • For example, presenting R as being within a range clearly demonstrates the uncertainty in the estimate. We particularly liked the presentation of uncertainty in the Welsh Government’s Technical Advisory Cell Monitoring document which uses a fan chart to show the uncertainty. The use of estimates to one decimal place is also commended as it also conveys the uncertainty of the estimates.

The narratives about the estimates of R

  • These are particularly helpful when they are simply worded, adopt visually engaging summaries with charts and infographics about the R-number, and are presented alongside data. An example of helpful referencing to source data is the Scottish Government’s presentation Coronavirus: Modelling the epidemic in Scotland: Issue 2
  • We see the value of these narratives as helping to make sense of the decisions about school closures, social distancing and other measures aimed at reducing the spread of the virus.

We expect that as more data becomes available and more knowledge is gained about the pandemic, there will naturally be improvements in the presentation of R. Our observations suggest that producers can improve the value and quality of their statistics about R by:

Adopting even clearer language and terminology to describe estimates of R

  • For example, describing the estimates of R as ‘a consensus value’ alongside a range is confusing without explaining what is meant by a ‘consensus value’. Producers need to be clear about messaging whether potentially small changes in ranges for the value of R are statistically different from previous week’s consensus.

Linking to clear and easily accessible supporting materials

  • Cited research should demonstrably support the evidence and ideas being put forward.

Clearly explaining the sensitivity of the models to key assumptions

  • Users of these statistics who are more analytical or who want more information about the data before they are confident in the analysis, may wish to understand the sensitivity of the estimates of R to key assumptions in the models.

We advise people, when speaking publicly or writing about R, adopt due accuracy and provide sufficient context to avoid misleading people. Key learning from the presentation of R for the UK and for devolved nations to date has been;

  • Be careful to help people see R in the context of other data for example alongside data on the number of people infected, and other relevant factors such declining or increasing infection rates.
  • Clearly communicate the extent and nature of any uncertainty in the estimates. For example, clearly state the uncertain nature of the estimates and avoiding talking about estimates as if they are fact. Also, there is a need for even greater caution when infection rates become very low.
  • Be clear that estimates of R come from modelled assumptions, which is why different models can yield different estimates. Good practice is, where possible, take account of the results from various models to discuss the range for the possible values of R.
  • Be aware that some groups access information on coronavirus through hearing the narrative about the latest alone and are unable to see slides or graphical information. This places a responsibility on commentators to be clear and accurate in what they say.