Coronavirus (COVID-19) Infection Survey pilot: England, 31 July 2020

Initial data from the COVID-19 Infection Survey. This survey is being delivered in partnership with IQVIA, Oxford University and UK Biocentre.

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Contact:
Email Nigel Henretty and James Cooper

Release date:
31 July 2020

Next release:
7 August 2020

1. Main points

  • In this bulletin, we refer to the number of current coronavirus (COVID-19) infections within the community population; community in this instance refers to private residential households, and it excludes those in hospitals, care homes or other institutional settings.

  • In this bulletin, 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.

  • An estimated 35,700 people (95% credible interval: 23,700 to 53,200) within the community population in England had COVID-19 during the most recent week, from 20 to 26 July 2020, equating to around 1 in 1,500 individuals.

  • There is now evidence to suggest a slight increase in the number of people in England testing positive on a nose and throat swab in recent weeks.

  • There is not enough evidence to say with confidence whether COVID-19 infection rates differ by region in England, nor whether infection rates have increased in different regions over the past six weeks.

  • During the most recent week (20 to 26 July 2020), we estimate there were around 0.78 new COVID-19 infections for every 10,000 people in the community population in England, equating to around 4,200 new cases per day (95% credible interval: 2,200 to 8,100).

  • Modelling of the rate of new infections over time suggests that there is now some evidence that the incidence of new infections has increased in recent weeks.

  • Between 26 April and 26 July, 6.2% of people tested positive for antibodies against SARS-CoV-2 on a blood test, suggesting they had the infection in the past.

How the data in this bulletin can be used

The data can be used for:

  • estimating the number of current positive cases in the community in England, including cases where people do not report having any symptoms

  • identifying differences in numbers of positive cases between different regions

  • estimating the number of new cases and change over time in positive cases in England

The data cannot be used for:

  • measuring the number of cases and infections in care homes, hospitals and other institutional settings

  • estimating the number of positive cases and new infections in smaller geographies, such as towns and cities

  • providing information about recovery time of those infected

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2. Number of people in England who had COVID-19

Exploratory modelling shows that the number of people in England testing positive for COVID-19 has increased slightly in recent weeks

During the most recent week of the study1, we estimate that 35,700 people in England had the coronavirus (COVID-19) (95% credible interval: 23,700 to 53,200)2. This equates to 0.07% (95% credible interval: 0.04% to 0.10%) of the population in England or around 1 in 1,500 people (95% credible interval: 1 in 2,300 to 1 in 1,000). This is based on exploratory modelling of throat and nose swab results.

Figure 1 presents modelled estimates of infection rates over time. While the percentage of individuals testing positive for COVID-19 has decreased since the start of the study (26 April 2020), there is now evidence to suggest that it has been rising slightly in recent weeks. Further analysis indicates that we can be confident3 that there was a real small increase in the most recent estimated daily infection rate when compared with the lowest daily rate from the last six weeks, which was 0.05% (95% credible interval: 0.04% to 0.07%) on 1 July 2020.

The modelled estimates for the latest six-week period are based on 116,026 swab tests collected over this period. During these weeks, 59 individuals from 58 households tested positive.

Using data from only the most recent six weeks in the model enables us to increase the speed at which we can produce estimates and will allow us to continue to provide timely results in the future. Estimates prior to the latest six weeks were modelled separately, based on complete data from last week's bulletin. The models are reproduced each week meaning that figures in this bulletin cannot be directly compared with figures provided in previous bulletins. The regression modelling was conducted by our research partners at the University of Oxford. More information about the methods used in the regression model is available in our methodology article.

As this is a household survey, our figures do not include people staying in hospitals, care homes or other institutional settings. In these settings, rates of COVID-19 infection are likely to be different. More information about rates of COVID-19 in care homes can be found in our analysis of the Vivaldi study.

Figure 1: Modelling shows evidence of a slight increase in COVID-19 infection rates in recent weeks

Estimated percentage of the population in England testing positive on nose and throat swabs for the coronavirus (COVID-19) daily since 15 June 2020

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Notes:

  1. These statistics refer to infections reported in the community, by which we mean private households. These figures exclude infections reported in hospitals, care homes or other institutional settings.
  2. These results are provisional and subject to revision.
  3. This analysis was produced by our research partners at the University of Oxford.
  4. The break distinguishes between the latest six-weeks estimates, and the earlier periods, which are based on complete data from last week’s bulletin.
  5. All estimates are subject to uncertainty; given that a sample is only part of the wider population. The model used to provide these estimates is a Bayesian model: these provide 95% credible intervals. A credible interval gives an indication of the uncertainty of an estimate from data analysis. 95% credible intervals are calculated so that there is a 95% probability of the true value lying in the interval.

We also present the estimates in non-overlapping 14-day periods. These estimates are available in the dataset that accompanies this bulletin. The 14-day estimates are provided for context, but cannot be directly compared with the modelled estimates in Figure 1. While the confidence intervals for these estimates are overlapping, they show a similar trend to the modelled estimates in Figure 1; that the percentage of people testing positive for COVID-19 has increased slightly in recent weeks.

In both the modelled and the 14-day non-overlapping estimates, infection rates are calculated based on whether participants have had at least one positive swab test during that period. By comparison, the 14-day non-overlapping period estimates presented in our bulletins on 9 July and earlier were based on the participant’s most recent test result in the period.

Notes for: Number of people in England who had COVID-19

  1. This is based on model estimates from the week’s midpoint, Thursday 23 July.
  2. These results are provisional and subject to revision.
  3. It is likely (with 89% probability) that the latest modelled rate is higher than the lowest modelled rate from the most recent six-week period (1 July).

More about coronavirus

  • Find the latest on coronavirus (COVID-19) in the UK.
  • All ONS analysis, summarised in our coronavirus roundup.
  • View all coronavirus data.
  • Find out how we are working safely in our studies and surveys.

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    3. Regional analysis

    There is not enough evidence to say there are differences in the percentage of people testing positive for COVID-19 in different regions of England

    There is not enough evidence to say with confidence that there is a difference in infection rates by regions during the most recent week of the study (20 to 26 July 2020). This is based on exploratory modelling of nose and throat swab test results conducted by our research partners at the University of Oxford.

    In the data used to produce these estimates, the number of people sampled in each region who tested positive for the coronavirus (COVID-19) is low relative to England overall. This means there is a high degree of uncertainty in the regional estimates for this period, as indicated by large credible intervals.

    Looking at trends over time, there is not enough evidence to say with confidence that COVID-19 infection rates have changed over the most recent six-week period in any region. There is some limited evidence that rates in London may have increased in recent weeks, however, because the credible intervals are wide we cannot be certain. The percentage of people testing positive by region was calculated using a similar modelling approach to the national daily estimates in Section 2: Number of people in England who had COVID-19.

    The analysis is conducted over a six-week period, which means some individuals testing positive for COVID-19 may move into and then out of the sample. This, coupled with the low number of positive tests by region, causes variability between estimates over time.

    Figure 3: There is not enough evidence to say for certain whether infection rates have increased in any region of England in recent weeks

    Estimated percentage of the population testing positive for coronavirus (COVID-19) on nose and throat swabs daily by region since 15 June 2020, England

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    Notes:

    1. These statistics refer to infections reported in the community, by which we mean private households. These figures exclude infections reported in hospitals, care homes or other institutional settings.
    2. Results for this period are provisional, as we are still receiving swab test results. This may result in further revisions to the figure.
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    4. Incidence rate

    There is now some evidence to suggest the number of people newly infected with COVID-19 has increased in recent weeks

    Based on exploratory modelling, we estimate that there were 0.78 new infections per 10,000 people followed for one day (95% credible interval during the most recent week of the study (20 July to 26 July). This equates to 4,200 new infections per day (95% credible interval: 2,200 to 8,100).

    Our findings suggest that there is now some evidence to suggest that the incidence of new cases has increased in recent weeks, following a low point of 0.34 new infections per 10,000 people followed for one day (95% credible interval: 0.25 to 0.46) during the week from 15 to 21 June. This follows an initial decrease in the incidence rate between May 2020, when the study began, and June 2020.

    The Bayesian model is based on the same approach used for the infection rate estimates in this bulletin. The model uses all swab test results to estimate the incidence rate for each different type of respondent (by age, sex and region). It is made to be representative of the overall population using population data. More information on the methodology of this approach is available.

    Figure 4: The latest exploratory modelling shows some evidence of an increase in the rate of new infections per day over recent weeks

    Estimated numbers of new infections with the coronavirus (COVID-19), England, based on tests conducted daily since 11 May 2020

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    Notes:

    1. Credible intervals are large at both ends of the plot because there is less information available. Although we know that individuals have been visited, there is a short delay in getting the associated swab results. The model does not include people when their next swab result is not known, so the sample size for the most recent days is smaller, resulting in wider credible intervals.
    2. This model does not control for household clustering, where multiple new cases derive from the same household.

    We also present the incidence rate in non-overlapping 14-day periods. These estimates are available in the dataset that accompanies this bulletin. The 14-day estimates are provided for context, but they cannot be directly compared with the modelled estimates. This is because our non-overlapping 14-day estimates of incidence are based on time at risk and new infections in each period, and so do not estimate a smooth change in risk day-by-day. Also, unlike the modelled estimates, the 14-day non-overlapping estimates are not weighted to be representative of the overall population in England. However, the 14-day estimates do show the same trend as the modelled estimates.

    The incidence rates for households, which controls for any household clustering in new infections, follow a similar trend as for individuals. These are based on 14-day non-overlapping period estimates. The household incidence rates can be found in the dataset.

    The incidence rate measures the occurrence of new cases of the coronavirus (COVID-19) and the calculation of this is defined in Section 9: Glossary. The incidence rate is not the same as the reproduction rate (R), which is the average number of secondary infections produced by one infected person.

    To calculate the estimated average number of people becoming newly infected per day, we multiply the daily incidence rate by the community population (54,628,600, see Coverage in Section 10: Measuring the data). We use the unrounded incidence rate to do this, so results will differ if calculated using the rounded estimates from the dataset.

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    5. Antibody data

    Around 6.2% of people who provided blood samples tested positive for antibodies to COVID-19

    As of 26 July 2020, 6.2% (95% confidence interval: 5.0% to 7.6%) of individuals aged 16 years and over tested positive for antibodies to the coronavirus (COVID-19) from any blood sample taken during the study. This equates to around 1 in 16 people. The estimate is weighted to be representative of the overall population, and suggests that around 2.8 million individuals (95% confidence interval: 2.3 million to 3.4 million) in England would test positive for antibodies if they were tested.1

    The analysis in this bulletin is based on test results from 4,840 individuals received since the start of the study on 26 April 2020. Of those who have provided blood samples, 241 tested positive for antibodies.

    One way the body fights infections like COVID-19 is by producing small particles in the blood called antibodies. It takes between two and three weeks for the body to make enough antibodies to fight the infection but once a person recovers, antibodies remain in the blood at low levels, although these levels can decline over time to the point that tests can no longer detect them. Having low levels of antibodies can help to prevent individuals from getting the same infection again, although other parts of the immune system can also protect people.

    We measure the presence of antibodies to understand who has had COVID-19 in the past, although the length of time antibodies remain at detectable levels in the blood is not fully known. It is also not yet known how having detectable antibodies, now or at sometime in the past, affects the chance of getting COVID-19 again.

    More information on how our estimates compare with other studies can be found in Section 10: Measuring the data.

    Notes for: Antibody data

    1. Changes in the rate of people testing positive for antibodies between bulletins should not be interpreted as a trend over time. This is because it relates to a change in the number of individuals whose blood has now been tested for antibodies. As of the 9 July publication, antibody data have been weighted. Estimates in earlier bulletins were unweighted.
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    6. Test sensitivity and specificity

    The estimates provided in Section 2: Number of people in England who had COVID-19 are for the percentage of the private-residential population testing positive for the 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, as COVID-19 is a new virus, our data and related studies provide an indication of what these are likely to be. To understand the potential impact of false-positives and false-negatives, we have estimated what prevalence would be in two scenarios using different test sensitivity and specificity rates. The results of these scenarios show that when these estimated sensitivity and specificity rates are taken into account, the prevalence rate would be similar to the main estimate presented in Section 2: Number of people in England who had COVID-19.

    For this reason, we do not produce prevalence estimates for every analysis, but we will continue to monitor the impacts of sensitivity and specificity in future.

    You can find more information on sensitivity and specificity in a paper written by the Office for National Statistics' academic partners and in our methods article.

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    7. COVID-19 Infection Survey data

    COVID-19 Infection Survey
    Dataset | Released 31 July 2020
    Latest findings from the pilot phase of the Coronavirus (COVID-19) Infection Survey.

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    8. Collaboration

    University of Oxford logo
    University of Manchester logo
    Public Health England logo
    Wellcome Trust logo

    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, Public Health England (PHE) 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

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    9. Glossary

    Community

    In this bulletin, we refer to the number of coronavirus (COVID-19) infections within the community. Community in this instance refers to private households, and it excludes those in hospitals, care homes or other institutional settings.

    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. 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. 95% credible intervals are calculated so that there is a 95% probability of the true value lying in the interval.

    False-positives and false-negatives

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

    Incidence rate

    The incidence rate is an estimate how often new cases of COVID-19 occur over a given period of time. In our study, it is calculated by dividing the number of times an individual has a positive test for the first time in the study, having first tested negative, by the total time everyone is in the study. We include the time people are in the study between successive negative tests for those who never have a positive test, and the time up to halfway between their last negative and first positive test for those that have a positive test. This reflects the fact that we do not actually know when a person first becomes positive, only when we tested them. Individuals who are positive when they join the study are not included in this calculation.

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    10. Measuring the data

    Data presented in this bulletin come from the Coronavirus (COVID-19) Infection Survey, which looks to identify the percentage of the population testing positive for COVID-19 and whether they have symptoms or not. The survey helps track the current extent of infection and transmission of COVID-19 among the population as a whole.

    This section of the bulletin provides a short summary of the study data and data collection methods. Our methodology article provides further information around the survey design, how we process data, and how data are analysed. The study protocol specifies the research for the study.

    Response rates

    Tables 1 and 2 provide information regarding responses to our survey. The current number of households invited to participate in the survey is 61,888, of which 25,521 have enrolled. In responding households, there are 54,400 eligible individuals.

    At the start of the pilot study, around 20,000 households were invited to take part, with the aim of achieving data from around 10,000 households. Since the end of May, additional households have been invited to take part in the survey each week (roughly 5,000 a week). This impacts the response rate as it takes time for those invited to respond and enrol.

    The response rates 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. However, as the likelihood of enrolment decreases over time, we have provided response rate information for those initially asked to take part at the start of the survey (Table 1) where response rates can be considered as relatively final. Separately, we provide response rates for those invited from 31 May (Table 2), where enrolment is still continuing.

    Coverage

    Only England is included in this pilot phase of the study. We intend for the full survey to expand the size of the sample over the next 12 months and look to cover people across all four UK nations. Only private residential households, otherwise known as the target population in this bulletin, are included in the sample. People in hospitals, care homes and other institutional settings are not included.

    The overall target population used in this study is 54,628,600.

    Analysing the data

    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.

    This is a pilot study where the analysis is developed at pace, and these quality enhancements may lead to minor changes in estimates, for example, the positive test counts across the study period.

    Other studies

    This study is one of a number of studies that look to provide information around the coronavirus pandemic within the UK.

    Department of Health and Social Care (DHSC) data

    Public Health England (PHE) present data on the total number of laboratory-confirmed cases in England, which capture the cumulative number of people in England who have tested positive for COVID-19. Equivalent data for Wales, Scotland and Northern Ireland are also available. These statistics present all known cases of COVID-19, both current and historical. The large sample size means it is possible to present known cases at local authority level.

    The NHS Test and Trace scheme was launched on 28 May 2020. The Test and Trace service ensures that anyone who develops symptoms of COVID-19 can quickly be tested to find out if they have the virus. It includes targeted asymptomatic testing of NHS and social care staff and care home residents. Additionally, it helps trace close recent contacts of anyone who tests positive for COVID-19 and, if necessary, notify them that they must self-isolate.

    In comparison with PHE data and NHS Test and Trace data, the statistics presented in this bulletin take a representative sample of the community population (those in private residential households) in England, including people who are not otherwise prioritised for testing. This means that we can estimate the number of people in the community population in England with COVID-19 who do not report symptoms. This is something that is currently missing from PHE and Test and Trace data.

    COVID Symptom Study (ZOE app and King's College London)

    The COVID Symptom Study app allows users to log their health each day, including whether or not they have symptoms of COVID-19. The study aims to predict which combination of symptoms indicate that someone is likely to test positive for COVID-19. The app was developed by the health science company ZOE with data analysis conducted by King's College London. Anyone over the age of 18 years can download the app and take part in the study. Respondents can report symptoms of children.

    The study estimates the total number of people with symptomatic COVID-19 and the daily number of new cases of COVID-19 based on app data and swab tests taken in conjunction with the Department of Health and Social Care (DHSC). The study investigates the "predictive power of symptoms", and so the data do not capture people who are infected with COVID-19 but who do not display symptoms.

    Unlike the data presented in this bulletin, the COVID Symptom Study is not a representative sample of the population. It is reliant on app users and so captures only some cases in hospitals, care homes and other communities where few people use the app. To account for this, the model adjusts for age and deprivation when producing UK estimates. The larger sample size allows for detailed geographic breakdown.

    Real-time Assessment of Community Transmission-1 and -2 (REACT-1 and -2)

    Like our study, the Real-time Assessment of Community Transmission-1 (REACT-1) survey involves taking swab samples to test for COVID-19 antigens to estimate the prevalence and transmission of the virus that causes COVID-19 in the community. The study currently involves around 120,000 participants aged five years and above, selected from a random cross-section sample of the general public from GP registration data, which allows for more detailed geographic breakdowns of infection rates than are currently possible within our study. Trends in infection by characteristics, such as age, sex, ethnicity, symptoms and key worker status, are also possible through the study. The REACT-2 study uses a finger prick test to generate data for antibody analysis.

    One of the main differences from our COVID-19 Infection Survey is that the REACT surveys do not require follow-up visits, as the study is interested primarily in prevalence at a given time point. Consequently, the incidence rate cannot be calculated from the REACT studies. It is also important to note that blood samples in the REACT-2 study are self-administered, rather than taken by a trained nurse, phlebotomist or healthcare assistant.

    PHE antibody data

    PHE also publish an estimate of the prevalence of antibodies in the blood in England using blood samples from healthy adult blood donors. PHE provide estimates by region and currently do not scale up to England. Estimates in this bulletin and those published by PHE are based on different tests; PHE estimates are based on testing using the Euroimmun assay method, while blood samples in our survey are tested for antibodies by research staff at the University of Oxford using a novel ELISA. For more information about the antibody test used in this bulletin, see the COVID-19 Infection Survey protocol.

    Next steps

    This edition of the bulletin presents headline analysis of the overall number of people infected with COVID-19, the regional positivity rate, incidence rate and antibodies. We provide headline figures once a week, to give regular, concise and high-quality information on COVID-19 within the community.

    Our recent release, Coronavirus infections in the community, offers more detailed analysis, which includes further exploration of the characteristics of those with COVID-19, such as age, sex, working location and occupation. We will also include further exploration of ethnicity when we have a large enough sample size to provide reliable analysis.

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    11. Strengths and limitations

    These statistics have been produced quickly in response to developing world events. The Office for Statistics Regulation, on behalf of the UK Statistics Authority, has reviewed them 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 the quality of data collected in the questionnaire. Information on the main sources of uncertainty are presented in our methodology article.

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    Contact details for this Statistical bulletin

    Nigel Henretty and James Cooper
    infection.survey.analysis@ons.gov.uk
    Telephone: +44 (0)203 9734761