Coronavirus (COVID-19): ONS Infection Survey – cumulative incidence data for Scotland – 22 April 2022

ONS Coronavirus (COVID-19) Infection Survey – Cumulative incidence of the number of people who have tested positive for COVID-19 – 22 April 2022

This article presents modelled estimates of the number of people who have had at least one episode of coronavirus (COVID-19) since the start of the UK COVID-19 Infection Survey on 27 April 2020 until 11 February 2022.

The Office for National Statistics (ONS) have published reference tables with the underlying data displayed in the charts in this publication, alongside a technical article about cumulative incidence estimates for England, Wales, Northern Ireland and Scotland, on the ONS website.

All results are provisional and are subject to revision.

Main Points

In Scotland, an estimated 2.7 million people had COVID-19 between 22 September 2020 and 11 February 2022 (90% credible intervals: 2.1 million to 3.3 million), equating to 51.5% of the population (90% credible intervals: 40.5% to 63.6%).

Across all four UK countries, the percentage of the population that have had COVID-19 since the start of the survey has increased at varying rates up to February 2022.

Information on this release

This release uses data from the COVID-19 Infection Survey to provide modelled estimates of the number of people who have ever had a COVID-19 infection during the time periods covered by the survey.

The start dates for this analysis relate to when the survey started, which is different for each country, and the analysis goes up to 11 February 2022. In Scotland, the survey started on 22 September 2020 so the estimate covers the period between 22 September 2020 and 11 February 2022.

The time periods covered for the other three nations are: 27 April 2020 to 11 February 2022 for England, 30 June 2020 to 11 February 2022 for Wales, and 27 July 2020 to 11 February 2022 for Northern Ireland. Therefore, the figures from the different nations are not directly comparable with each other as they refer to different time periods.

The people included in the survey were aged two years and over and were living in private households; those in hospitals, care homes and/or other communal establishments were not included.

In epidemiology, daily prevalence is the number of people with an infection on a given day, whilst incidence is the number of people newly infected on a given day. In the survey, the ONS estimates both the number of people in the population who would test positive on a nose and throat swab (positivity) and the number of people who would be newly positive on a nose and throat swab each day (incidence) using both positive and negative swab results. Positivity refers to the proportion or number of people who would test positive on any given day if the whole population was sampled. Positivity is not the true number infected on a given day, it is those testing positive on a given day.

To estimate how many people have ever had COVID-19, the ONS first estimates the number of people who would test positive for the first time on any given day and then aggregate this over time. The ONS first estimate the daily proportion of the population who would test positive with their first known COVID-19 infection (if they were tested). An established incidence methodology provides an estimate of the daily numbers becoming test-positive for the first time. This is then aggregated to estimate the number of people who have ever been test-positive (see note 8 for the ONS definition of a new episode of infection).

Further information on the methodology, comparisons of these estimates with other sources, and data sources and quality, is available in the technical article on the ONS website.

Estimates of cumulative incidence by country

The cumulative incidence analysis produces an estimate of the number of people who have been infected with COVID-19 since the start of the COVID-19 infection survey to 11 February 2022 for each of the four UK countries (see note 6 for further information about cumulative incidence).

In Scotland, an estimated 2.7 million people had COVID-19 between 22 September 2020 and 11 February 2022 (90% credible intervals: 2.1 million to 3.3 million), equating to 51.5% of the population (90% credible intervals: 40.5% to 63.6%) (see note 7 for further information about credible intervals) (see note 1).

Estimates for the other nations of the UK are as follows (see note 2):

• in England, an estimated 38.5 million people had coronavirus (COVID-19) between 27 April 2020 and 11 February 2022 (90% credible intervals: 36.0 million to 41.2 million), equating to 70.7% of the population (90% credible intervals: 66.0% to 75.6%).
• in Wales, an estimated 1.7 million people had COVID-19 between 30 June 2020 and 11 February 2022 (90% credible intervals: 1.3 million to 2.1 million), equating to 56.0% of the population (90% credible intervals: 44.3% to 69.4%).
• in Northern Ireland, an estimated 1.3 million people had COVID-19 between 27 July 2020 and 11 February 2022 (90% credible intervals: 1.0 million to 1.7 million), equating to 72.2% of the population (90% credible intervals: 56.0% to 90.9%).

Figure 1 shows modelled daily estimates of the cumulative percentage of the private residential population in Scotland who have tested positive for COVID-19 during the survey period 22 September 2020 and 11 February 2022.

Across all four UK countries, the percentage of the population that have had COVID-19 since the start of the survey has increased at varying rates up to February 2022 (see note 2).

Figure 1: Estimated cumulative percentage of the population in Scotland who have tested positive for COVID-19, between 22 September 2020 and 11 February 2022

To develop the analysis presented in this article further, the ONS plan to apply its updated definition of an episode of infection to the current method. The updated definition of an episode reflects the shorter time between re-infections that have occurred during the Omicron variants period, compared with earlier variants (see note 13).

The ONS are also developing models to estimate the percentage of people who have had COVID-19 during the study period by age group.

Methodology and further information

1. The people included in the survey were aged two years and over and were living in private households; those in hospitals, care homes and/or other communal establishments were not included.
2. The time periods covered for the other three nations are: 27 April 2020 to 11 February 2022 for England, 30 June 2020 to 11 February 2022 for Wales, and 27 July 2020 to 11 February 2022 for Northern Ireland. Therefore, the figures from the different nations are not directly comparable with each other as they refer to different time periods.
3. All results are provisional and subject to revision.
4. The sample includes 535,116 people in the UK (Coronavirus Infection Survey (CIS) participants), who had one or more nose and throat swabs to test for COVID-19, each participant was regularly tested during the duration of their time in the study. The swabs were tested using polymerase chain reaction (PCR). The ONS use COVID-19 infections to mean testing positive for SARS-CoV-2, the coronavirus causing COVID-19 in the UK.
5. The ONS take all positive and negative tests in the survey and apply statistical modelling techniques to estimate the number of people who have had COVID-19 in the population, in each of the four UK nations for the duration of the survey.
6. Cumulative incidence is the percentage of individuals experiencing an outcome of interest over a specific time period. In this case, the percentage of individuals testing positive for COVID-19 over a specific time period.
7. A credible interval gives an indication of the uncertainty of an estimate from data analysis. The 90% credible intervals are calculated so that there is a 90% probability of the true value lying in the interval.
8. To differentiate between subsequent infections in the same person, and to estimate the length of time a person would test positive for, the ONS defines a new episode of infection by:

• • a new positive test which occurs 120 days or more after an individual's first positive test in the survey and their most recent prior test result was negative,
  • or, if 120 days has not passed since their first positive test in the survey, the individual's last positive test has been followed by four consecutive negative tests.

Any other positive tests are counted as being within the same infection episode. In this analysis, to estimate the daily proportion of the population who would test positive with their first COVID-19 infection, the ONS reclassify any positive tests after the first infection episode as ‘negative’. Reclassifying positive tests in observed subsequent infection episodes allows the ONS to include only first infections to estimate positivity with a first COVID-19 infection, whilst retaining in the population people who have had COVID-19 before.

9. The ONS applies a general additive mixed model (GAMM) to obtain a smoothed time series of positivity and post-stratify using age and region to obtain a nationally representative estimate. For England, owing to computing constraints, two models were run from 27 April 2020 to 11 May 2021 and 12 April 2021 to 11 February 2022. These two models overlap by 30 days from 12 April 2021 to 11 May 2021 to allow for the resulting incidence estimates series (see below) to be smoothly spliced together.
10. To obtain the daily incidence of new positive infection episodes, the ONS requires an estimate of how long a person with a COVID-19 infection will test positive for. Using data from people who have tested positive in the survey, the ONS can estimate the time between a person first testing positive and when they would first test negative again. This duration varies from person to person and so ONS estimate and use its distribution and allow for the duration distribution to vary over the course of the pandemic.
11. The ONS combines the estimates of positivity and duration to obtain daily incidence. It transforms the ’first infection episode’ positivity series into daily incidence of ‘first episodes’ of being test-positive. In general, incidence and duration can be used straightforwardly to give prevalence. The reverse process of estimating incidence is called ‘deconvolution’.
12. In this analysis, having reclassified the data so only positive tests in first episodes are counted positive, positivity on any given day is the sum of those first testing positive on previous days who are still test-positive on that particular day. A single linear equation relates prior (unknown) daily incidence, and corresponding (known) durations, to each day’s (known) positivity. Combining multiple days of positivity gives a system of linear equations which can be solved mathematically to give the unknown daily incidences. The daily incidences are cumulated to give the estimated number of people who have ever been test-positive over time.
13. The ONS definition of an infection episode assumes that individuals can test positive for up to 120 days within the same infection. Since the Omicron variants became dominant, the ONS and others have observed a high number of re-infections, many shortly after Delta infections, suggesting that this is less appropriate. Since the definition of an infection episode is used to estimate the time people will test positive for, changing the definition to allow for re-infections happening much more quickly after a previous infection will reduce the estimated number of days an individual will test positive for. This will increase the estimates of incidence, meaning the current estimates would be biased downwards.
14. The method assumes first infection episodes identified in the COVID-19 Infection Survey data are truly the first time a person had COVID-19. While this will have been (nearly) true at the start of the pandemic, as more people have had COVID-19 and then been re-infected, an increasing proportion of ‘first infection episodes’ will in fact be re-infections where the first infection was not identified in the survey. This means estimates of first-infection-positivity will be inflated (by inclusion of some second or later infections which we were unable to identify as such), and hence the current estimates of how many people have had COVID-19 since the start of the survey could be biased upwards. The credible intervals do not allow for this uncertainty.
15. Infections before the survey started in the respective UK country are not included. This means current estimates will be biased downwards from the true figure, particularly in Wales, Northern Ireland and Scotland where data collection started later, but will not change the shape of the curves appreciably.
16. The beginning and ends of the time series are subject to less information and instability and subject to greater relative uncertainty which may not be fully captured in the credible intervals. The ONS has removed the last 14 days of the series to attempt to mitigate against some of this. The net effect on the bias is unclear.
17. The ONS do not know the exact specificity (percentage of true negatives that test negative) and sensitivity (percentage of true positives that test positive) of the polymerase chain reaction (PCR) test, the credible intervals do not adjust for this uncertainty. As this uncertainty is primarily driven by the uncertainty in the sensitivity of the tests, and given the high specificity of the test, true positivity and duration of positivity are both likely to be biased slightly downwards, but the deconvolution means that the net bias could go in either direction.