Coronavirus (COVID-19) Infection Survey, UK: 4 February 2022

To ensure our latest estimates are available at the earliest opportunity during this period of high infections of coronavirus (COVID-19), we will be publishing early headline results on Wednesdays. Our latest headline results were published on Wednesday 2 February 2022. The analysis in this bulletin provides further breakdowns of these results for the same period and a longer data time series.

• In England, the percentage of people testing positive for COVID-19 remained high in the week ending 29 January 2022, with substantial variation in trends across different regions; we estimate that 2,633,100 people in England had COVID-19 (95% credible interval: 2,544,100 to 2,725,100), equating to around 1 in 20 people.
• In Wales, the percentage of people testing positive for COVID-19 increased in the week ending 29 January 2022; we estimate that 139,000 people in Wales had COVID-19 (95% credible interval: 119,800 to 159,300), equating to around 1 in 20 people.
• In Northern Ireland, the percentage of people testing positive for COVID-19 increased in the week ending 29 January 2022; we estimate that 136,300 people in Northern Ireland had COVID-19 (95% credible interval: 118,200 to 155,200), equating to around 1 in 15 people.
• In Scotland, the percentage of people testing positive for COVID-19 decreased in the two weeks up to 29 January 2022, but the trend was uncertain in the most recent week; we estimate that 185,100 people in Scotland had COVID-19 (95% credible interval: 162,100 to 210,800), equating to around 1 in 30 people.

Within this bulletin, we summarise some of the latest results from the Coronavirus (COVID-19) Infection Survey. For more detailed information on our methods see our COVID-19 Infection Survey methodology article. You can also find out more about comparing methods used in the Coronavirus (COVID-19) Infection Survey and NHS Test and Trace in our article, to better understand comparisons in data sources.

About this bulletin

In this bulletin, we refer to the number of current COVID-19 infections within the population living in private residential households. We exclude those in hospitals, care homes and/or other communal establishments. In communal establishments, rates of COVID-19 infection are likely to be different. More information about the COVID-19 pandemic from the Office for National Statistics (ONS) and other sources can be found in our Coronavirus (COVID-19) latest insights.

The positivity rate is the percentage of people who have tested positive for COVID-19 on a polymerase chain reaction (PCR) test at a point in time. We use current COVID-19 infections to mean testing positive for SARS-CoV-2, with or without having symptoms, on a swab taken from the nose and throat. This is different to the incidence rate, which is a measure of only the new PCR positive cases in a given time period. All analysis was produced with our research partners at the University of Oxford.

Our estimates are based on confirmed positive test results. The remaining swabs are either negative, which are included in our analysis, or are inconclusive, which are not included in our analysis. Some swabs are test failures, which are also not included in our analysis. The impact of excluding inconclusive results on our estimates of positive infections is likely to be very small and unlikely to affect the trend.

Early management information from the Coronavirus (COVID-19) Infection Survey is made available to government decision-makers to inform their response to COVID-19. Occasionally we may publish figures early if it is considered in the public interest. We will ensure that we pre-announce any ad hoc or early publications as soon as we can. These will include supporting information where possible to aid user understanding. This is consistent with guidance from the Office for Statistics Regulation (OSR).

How the data in this bulletin can be used

The data can be used for:

• estimating the number of positive cases among the population living in private households, including cases where people do not report having any symptoms
• identifying differences in numbers of positive cases between UK countries and different regions in England
• estimating the number of new cases and change over time in positive cases

The data cannot be used for:

• measuring the number of cases and infections in care homes, hospitals and/or other communal establishments
• providing information about recovery time of those infected

In England, the percentage of people testing positive for coronavirus (COVID-19) remained high in the week ending 29 January 2022. In Wales and Northern Ireland, the percentage of people testing positive for COVID-19 increased in the same week. In Scotland, the percentage of people testing positive for COVID-19 decreased in the two weeks up to 29 January 2022, but the trend was uncertain in the most recent week. Our reported headline positivity estimates contain Omicron (BA.1) and all other variants.

All estimates are based on statistical modelling of the trend in rates of positive nose and throat swab results. All our estimates are subject to uncertainty given that a sample is only part of the wider population. Additionally, the estimates for the very latest days may change as more test results are received. Therefore, caution should be taken in over-interpreting any small movements in the latest trends.

Figure 1: The percentage of people testing positive for COVID-19 remained high in England, increased in Wales and Northern Ireland, and the trend was uncertain in Scotland in the week ending 29 January 2022

Estimated percentage of the population testing positive for coronavirus (COVID-19) on nose and throat swabs, UK, 6 February 2021 to 29 January 2022

Notes:

1. Modelled results are provisional and subject to revision.
2. These statistics refer to infections occurring in private households.
3. All estimates are subject to uncertainty, a credible interval gives an indication of the uncertainty of an estimate from data analysis.
4. Official reported estimates are plotted at a reference point believed to be most representative of the given week.
5. Official estimates present the best estimate at that point in time. Modelled estimates are used to calculate the official reported estimate. The model smooths the series to understand the trend and is revised each week to incorporate new test results, providing the best indication of trend over time.
6. Official estimates are displayed over a rolling year up to the most recent week. The full time series of our official estimates from 27 April 2020 onwards are available in the Coronavirus (COVID-19) Infection Survey datasets.

Download the data

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About our estimates

Our headline estimates of the percentage of people testing positive in England, Wales, Northern Ireland and Scotland are the latest official estimates. We include different measures to support our estimation and this section outlines the appropriate uses of all the approaches.

Official estimates should be used to understand the positivity rate for a single point in time. This is based on the modelled estimate for the latest week and is our best and most stable estimate, used in all previous outputs. The modelled estimate is more suited to understanding the recent trend. This is because the model is regularly updated to include new test results and smooths the trend over time. These modelled estimates can be found in the Coronavirus (COVID-19) Infection Survey datasets.

The estimates for non-overlapping 14-day periods (which underpin our modelled official estimates) and the unweighted sample counts are included in the Coronavirus (COVID-19) Infection Survey datasets. These estimates are produced using a different method of weighting to the model and are available for people who wish to compare infection levels over time in this way. For more information on our methods and quality surrounding the estimates please see our COVID-19 Infection Survey methods article and Quality and Methodology Information report.

All estimates presented in this bulletin are provisional results. As swabs are not necessarily analysed in date order by the laboratory, we have not yet received test results for all swabs taken on the dates included in this analysis. Estimates may therefore be revised as more test results are included.

In the week ending 29 January 2022, the percentage of people testing positive for coronavirus (COVID-19) remained high across all regions of England, but trends varied substantially.

The percentage testing positive increased in the South East and South West, and decreased in the North East, North West, Yorkshire and The Humber and the East Midlands in the week ending 29 January 2022. The percentage of people testing positive decreased in the West Midlands and London in the two weeks up to 29 January 2022, but the trends were uncertain in the most recent week. The trend in the percentage testing positive was also uncertain in the East of England in the week ending 29 January 2022. Caution should be taken in over-interpreting any small movements in the latest trend.

Figure 2: The percentage of people testing positive for COVID-19 remained high across all regions of England, but the trends varied substantially in the week ending 29 January 2022

Modelled daily percentage of the population testing positive for coronavirus (COVID-19) on nose and throat swabs by region, England, 19 December 2021 to 29 January 2022

Notes:

1. All results are provisional and subject to revision.
2. These statistics refer to infections occurring in private households.
3. All estimates are subject to uncertainty, a  credible interval gives an indication of the uncertainty of an estimate from data analysis.
4. The credible intervals widen slightly at the end as there is a delay between the swab being taken and reporting of results.
5. The percentage of people testing positive by region was calculated using a similar modelling approach to the national daily estimates in  Section 2: Percentage of people who had COVID-19 in England, Wales, Northern Ireland and Scotland.
6. The analysis is conducted over a six-week period, which means specific positive cases move into and then out of the sample. This causes variability between estimates over time, which is expected given the lower number of positive tests within each region, compared with England as a whole.
7. We describe trends by comparing the probability that the estimate for the reference day is higher or lower than the estimate for 7 and 14 days prior.

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Estimates for non-overlapping 14-day periods (which underpin our modelled estimates) by region are available in our Coronavirus (COVID-19) Infection Survey datasets and are provided as an alternative measure over time for context.

Sub-regional analysis of the UK

The percentage of people testing positive for COVID-19 varied across sub-regions of the UK. Figure 3 presents modelled estimates for sub-regions of all UK countries in the week ending 29 January 2022.

Figure 3: The percentage of people testing positive for COVID-19 by sub-regions of the UK

Modelled percentage of the population testing positive for coronavirus (COVID-19) on nose and throat swabs by sub-regional geography, UK, 23 to 29 January 2022

Notes:

1. All results are provisional and subject to revision.
2. These statistics refer to infections occurring in private households.
3. All estimates are subject to uncertainty, a  credible interval  gives an indication of the uncertainty of an estimate from data analysis.
4. Sub-regional estimates are based on a different model to our headline estimates. Our sub-regional estimates are calculated as an average over a seven-day period and should not be compared with our headline positivity estimates, which are for a single reference date. Therefore, the subregional figures may differ from the headline estimates because they are averaged over a longer time period. If a trend is changing quickly, the figures shown in Figure 3 may not reflect the change we are seeing in our headline estimates.
5. We have modelled Wales and Scotland separately to England. Northern Ireland is always modelled separately as it does not share a land border with the other UK countries, and so requires a different geospatial modelling approach.
6. The colour scale has been adjusted from the 7 January 2022 publication onwards to accommodate increased levels of infections in the analysis and so cannot be compared with sub-regional charts in previous bulletins.

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Age analysis by category for England

Our age categories separate children and young people by school age:

• "aged 2 years to school Year 6" includes children in primary school and below
• "school Year 7 to school Year 11" includes children in secondary school
• "school Year 12 to those aged 24 years" includes young adults who may be in further or higher education

This means that those aged from 11 to 12 years and those aged 16 to 17 years have been split between different age categories depending on whether their birthday is before or after 1 September.

The percentage of people testing positive for coronavirus (COVID-19) remained very high for those aged 2 years to school Year 6 in the week ending 29 January 2022. In the same week, the percentage of people testing positive increased for those in school Year 7 to school Year 11, and for those aged 35 to 49 years. The percentage of people testing positive decreased for those aged 25 to 34 years and aged 50 and over in the week ending 29 January 2022. The percentage of people testing positive decreased for those in school Year 12 to age 24 years in the two weeks up to 29 January 2022, but the trend was uncertain in the most recent week.

In the week ending 29 January 2022, the percentage of people testing positive for COVID-19 varied substantially across age groups, being highest for those aged 2 years to school Year 6 at 13.09% (95% confidence interval: 12.13% to 14.07%) and lowest for those aged 70 years and over at 1.97% (95% confidence interval: 1.78% to 2.17%).

Figure 4: The percentage of people testing positive for COVID-19 varied substantially across age groups, remaining highest for those aged 2 years to school Year 6 in the week ending 29 January 2022

Modelled daily percentage of the population testing positive for coronavirus (COVID-19) on nose and throat swabs by age group, England, 19 December 2021 to 29 January 2022

Notes:

1. All results are provisional and subject to revision.
2. These statistics refer to infections occurring in private households.
3. All estimates are subject to uncertainty, a  credible interval gives an indication of the uncertainty of an estimate from data analysis.
4. Credible intervals widen slightly at the end as there can be a delay between the swab being taken and reporting of results. Because of this greater uncertainty in the most recent days, we report latest figures based on the reference day for that week.

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Estimates for non-overlapping 14-day periods (which underpin our modelled estimates) by age group are available in our Coronavirus (COVID-19) Infection Survey datasets and are provided as an alternative measure over time for context.

We are unable to produce the same grouped analysis as presented in Figure 4 for the devolved administrations because of smaller sample sizes within each age group. We are able to produce analysis on positivity by single year of age for Wales, Northern Ireland and Scotland using a different model and these estimates can be found in the following section and the Coronavirus (COVID-19) Infection Survey datasets.

Age analysis by single year of age over time by country

In this section, we present modelled daily estimates of the percentage testing positive for COVID-19. These estimates are by single year of age over time for England, Wales, Northern Ireland and Scotland from 19 December 2021 to 29 January 2022 and are presented in Figure 5. They are produced using a different method to the grouped age analysis for England presented previously and are therefore not comparable. Caution should be taken in over-interpreting small movements in positivity day to day.

The data presented in Figure 5 for England suggest that the percentage testing positive remained high for those of primary school age and decreased in the older ages. Uncertainty is high for all ages in Wales, Northern Ireland and Scotland in comparison with England because of comparatively smaller sample sizes, as indicated by wider confidence intervals.

In Wales, the percentage testing positive increased for primary school aged children, but trends were uncertain for other ages. In Northern Ireland, the percentage testing positive increased across all ages. In Scotland, the percentage testing positive increased for primary school aged children and decreased for young adults. The trends were uncertain for secondary school ages, as well as the older ages.

Further analysis on age for Wales, Northern Ireland and Scotland is published by their respective statistical agencies. Analysis for Wales is published in English and Welsh.

Figure 5: The percentage testing positive for COVID-19 by single year of age over time for England, Wales, Northern Ireland and Scotland

Modelled daily percentage of the population testing positive for coronavirus (COVID-19) on nose and throat swabs by single year of age, UK, 19 December 2021 to 29 January 2022

Notes:

1. All results are provisional and subject to revision.
2. These statistics refer to infections occurring in private households.
3. These estimates use a different method to previous modelled daily estimates of the percentage testing positive by age group for England and are therefore not comparable.
4. Uncertainty is high for all ages in Wales, Northern Ireland and Scotland in comparison with England because of smaller sample sizes, as indicated by wider confidence intervals.

Download the data

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The incidence rate is a measure of new polymerase chain reaction (PCR)-positive cases per day per 10,000 people in a given time period. In this section we look at an earlier time period to our percentage testing positive analysis. We include estimates of the incidence rate in our Coronavirus (COVID-19) Infection Survey datasets and in Table 2.

In the week ending 15 January 2022, the number of new PCR-positive COVID-19 cases per day continued to decrease in England, Wales and Scotland. In Northern Ireland, the trend in the number of new PCR-positive COVID-19 cases per day was uncertain in the week ending 15 January 2022. Credible intervals are wider for Wales, Northern Ireland and Scotland because of relatively smaller sample sizes, and care should be taken in interpreting results.

The reference date used for our official estimates of incidence of PCR-positive cases is 14 days before the positivity reference day, meaning that there is a two-week lag between the incidence estimate and the positivity estimate. This is necessary as estimates later than this date are more likely to change as we receive additional data. While we believe that the incidence estimates are useful, they can be volatile and subject to change as more data become available. For more information on how we calculate estimates of incidence please see COVID-19 Infection Survey: methods and further information.

Each week, we publish Cycle-threshold (Ct) values, which indicate viral load and act as a proxy for the strength of the virus. We also break down infections by variant based on gene positivity patterns from our swab tests.

The "Cycle threshold", known as a Ct value, is used as a proxy for the quantity of the virus, also known as the viral load. The higher the viral load, the lower the Ct value. These values are helpful for monitoring the strength of the virus. The Ct values of coronavirus (COVID-19) positive tests are provided in the technical dataset that accompanies this bulletin.

The Omicron variant currently dominant in the UK is BA.1. This variant has changes in one of the three genes that the coronavirus swab used in the survey tests detects, known as the S-gene, which means the S-gene is no longer detected by the current test. When there is a high viral load (for example, when a person is most infectious), absence of the S-gene in combination with the presence of the other two genes (ORF1ab and N-genes) is a reliable indicator of this Omicron variant (BA.1). However, as the viral load decreases (for example, if someone is near the end of their recovery from the infection), the absence of the S-gene is a less reliable indicator of this Omicron variant. The sub-variant Omicron BA1.1 also mostly has gene pattern ORF1ab + N. Therefore, gene pattern matching used in our main variant analysis cannot distinguish between Omicron BA.1 and Omicron BA.1.1.

In contrast, the Omicron sub-variant BA.2 does not have changes in the S-gene, and therefore the detection of all three genes, or the S-gene and either ORF1ab or N is usually a reliable indicator of this sub-variant of Omicron. Delta also does not have changes in the S-gene, and therefore has identical gene patterns to Omicron BA.2. This means that gene pattern matching cannot distinguish between Omicron BA.2 cases and Delta cases. For this reason, we label cases with gene patterns ORF1ab + N + S, ORF1ab + S and N + S as “not compatible with Omicron BA.1” in our main variant analysis. Our genome sequencing analysis suggests that a clear majority of cases with these gene patterns are now Omicron BA.2, with relatively few being Delta. For this reason, “not compatible with Omicron BA.1” estimates are now likely to mostly reflect trends in BA.2, although Delta may still be having a very small impact.

The World Health Organization (WHO) have defined names for variants of concern. These are variants that the UK government has under surveillance. You can find out more in the latest SARS-CoV-2 variants of concern and variants under investigation in England briefing document.

UK variants of concern:

• Alpha: B.1.1.7
• Beta: B.1.351
• Gamma: P.1
• Delta: B.1.617.2 and its genetic descendants
• Omicron: B.1.1.529 (which includes sublineages BA.1, BA.1.1, BA.2 and BA.3)

Since the end of December, infections compatible with the Omicron BA.1 variant have been the most common in all four UK countries. Our main variant analysis shows that in Northern Ireland, there was a notable increase in the percentage of people testing positive not compatible with Omicron BA.1 in the week ending 29 January 2022; there was also a small increase in England and early signs of an increase in Wales. In the same week, the trend in the percentage of people testing positive not compatible with Omicron BA.1 was uncertain in Scotland. This group of positives will include Omicron BA.2 and Delta cases; the majority of recent cases will be Omicron BA.2 based on our genetic sequencing.

The most recent week shows that 96.6% of all sequenced COVID-19 infections were the Omicron BA.1 variant or its sub-variants, and 2.8% were the Omicron BA.2 sub-variant. Our latest complete sequenced data lags our main variant analysis and these percentages are an average over a whole week to 23 January 2022.

Because of the high proportion of a single variant across most UK countries, we have not included charts showing breakdown by variant in this bulletin. However, our variant analysis by country and by region has been updated and can be found in our accompanying technical dataset. Our main variant analysis is for a reference day, and therefore is not directly comparable with the sequencing data. We will continue to monitor infections by variant and will reintroduce the charts when considered helpful.

Coronavirus (COVID-19) Infection Survey: technical dataset contains several tables relating to analysis on variants, including the genetic lineages of the virus seen in the samples we sequence. More information on how we measure variants from positive tests on the survey can be found in our Understanding COVID-19 variants blog. Our COVID-19 Infection Survey methods article gives more detail about how we sequence the virus' genetic material.

The sequencing is produced by Northumbria University and analysis is produced by research partners at the University of Oxford. Of particular note are Dr Katrina Lythgoe, Dr David Bonsall, Dr Tanya Golubchik, and Dr Helen Fryer. Genome sequencing is funded by the COVID-19 Genomics UK (COG-UK) consortium. COG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research and Innovation (UKRI), the National Institute of Health Research (NIHR), and Genome Research Limited operating as the Wellcome Sanger Institute.

The estimates provided in Sections 2 to 6 are for the percentage of the private-residential population testing positive for coronavirus (COVID-19), otherwise known as the positivity rate. We do not report the prevalence rate. To calculate the prevalence rate, we would need an accurate understanding of the swab test's sensitivity (true-positive rate) and specificity (true-negative rate).

While we do not know the true sensitivity and specificity of the test, our data and related studies provide an indication of what these are likely to be. In particular, the data suggest that the false-positive rate is very low – under 0.005%. We do not know the sensitivity of the swab test. However, other studies suggest that sensitivity (the rate of true-positive test results) may be somewhere between 85% and 98%.

You can find more information on sensitivity and specificity in our COVID-19 Infection Survey methods article and our blog that explains why we trust the data from the COVID-19 Infection Survey. You can find more information on the data suggesting that our test's false-positive rate is very low in a paper written by academic partners at the University of Oxford.

The Coronavirus (COVID-19) Infection Survey analysis was produced by the Office for National Statistics (ONS) in collaboration with our research partners at the University of Oxford, the University of Manchester, UK Health Security Agency (UK HSA) and Wellcome Trust. Of particular note are:

• Sarah Walker - University of Oxford, Nuffield Department for Medicine: Professor of Medical Statistics and Epidemiology and Study Chief Investigator

• Koen Pouwels - University of Oxford, Health Economics Research Centre, Nuffield Department of Population Health: Senior Researcher in Biostatistics and Health Economics

• Thomas House - University of Manchester, Department of Mathematics: Reader in Mathematical Statistics

Confidence interval

A confidence interval gives an indication of the degree of uncertainty of an estimate, showing the precision of a sample estimate. The 95% confidence intervals are calculated so that if we repeated the study many times, 95% of the time the true unknown value would lie between the lower and upper confidence limits. A wider interval indicates more uncertainty in the estimate. Overlapping confidence intervals indicate that there may not be a true difference between two estimates. For more information, see our methodology page on statistical uncertainty.

Credible interval

A credible interval gives an indication of the uncertainty of an estimate from data analysis. The 95% credible intervals are calculated so that there is a 95% probability of the true value lying in the interval.

Cycle threshold (Ct) values

The strength of a positive coronavirus (COVID-19) test is determined by how quickly the virus is detected, measured by a cycle threshold (Ct) value. The lower the Ct value, the higher the viral load and stronger the positive test. Positive results with a high Ct value can be seen in the early stages of infection when virus levels are rising, or late in the infection, when the risk of transmission is low.

False-positives and false-negatives

A false-positive result occurs when the tests suggest a person has COVID-19 when in fact they do not. By contrast, a false-negative result occurs when the tests suggest a person does not have COVID-19 when in fact they do. For more information on false-positives and false-negatives, see our methods article and our blog.

Incidence rate

The incidence rate is a measure of the estimated number of new polymerase chain reaction (PCR)-positive cases per day per 10,000 people at a given point in time. It is different to positivity, which is an estimate of all current PCR positive cases at a point in time, regardless of whether the infection is new or existing.

Reference dates

We aim to provide the estimates of positivity rate (the percentage of those who test positive) and incidence that are most timely and most representative of each week. We decide the most recent week we can report on based on the availability of test results for visits that have already happened, accounting for the fact that swabs have to be couriered to the labs, tested and results returned. On most occasions, the reference dates align perfectly, but sometimes this is not feasible. This week, the reference week for positivity is 23 to 29 January 2022 for all UK countries.

Within the most recent week, we provide an official estimate for positivity rate based on a reference point from the modelled trends. For positivity rates, we can include all swab test results, even from the most recent visits. Therefore, although we are still expecting further swab test results from the labs, there were sufficient data for the official estimate for infection to be based on a reference point after the start of the reference week. To improve stability in our modelling while maintaining relative timeliness of our estimates, we are reporting our official estimates based on the midpoint of the reference week. This week, the reference day for positivity rates is Wednesday 26 January 2022 for all UK countries.

The reference date used for our official estimates of incidence of polymerase chain reaction (PCR)-positive cases is 14 days prior to the positivity reference day. This is necessary as estimates later than this date are more likely to change as we receive additional data. This week, the reference week for incidence is 9 to 15 January 2022 and the reference day is Wednesday 12 January 2022 for all UK countries.

Response rates

Response rates for England, Wales, Northern Ireland and Scotland cannot be regarded as final response rates to the survey since those who are invited are not given a time limit in which to respond, and different modes of sampling are not comparable. Response rates for each nation are found in the Coronavirus (COVID-19) Infection Survey: technical dataset. We provide response rates separately for the different sampling phases of the study. Additional information on response rates can be found in our COVID-19 Infection Survey methods article.

Survey fieldwork

Survey fieldwork for the pilot study began in England on 26 April 2020. In Wales, fieldwork began on 29 June 2020, in Northern Ireland fieldwork began on 26 July 2020 and in Scotland fieldwork began on 21 September 2020.

Sub-regional geographies

We have presented modelled estimates for the most recent week of data at the sub-regional level. To balance granularity with statistical power, we have grouped together local authorities into COVID-19 Infection Survey sub-regions. The geographies are a rules-based composition of local authorities, and local authorities with a population over 200,000 have been retained where possible.

The boundaries for these COVID-19 Infection Survey sub-regions can be found on the Open Geography Portal.

Other Coronavirus Infection Survey (CIS) analysis and studies

This study is one of a number of studies that look to provide information around the coronavirus pandemic within the UK. For information on other studies see Section 11: Measuring the data in our previous bulletin dated 30 April 2021.

These statistics have been produced quickly in response to developing world events. The Office for Statistics Regulation (OSR), on behalf of the UK Statistics Authority, has reviewed them on 14 May 2020 and 17 March 2021 against several important aspects of the Code of Practice for Statistics and regards them as consistent with the Code's pillars of trustworthiness, quality and value.

The estimates presented in this bulletin contain uncertainty. There are many sources of uncertainty, including uncertainty in the test, in the estimates and in the quality of data collected in the questionnaire. Information on the main sources of uncertainty are presented in our Coronavirus (COVID-19) Infection Survey Quality and Methodology Information report, our methodology article, and our blog that explains why we trust the data from the COVID-19 Infection Survey.