- This analysis was produced by academics outside of the Office for National Statistics (ONS), meaning the methodology used differs from existing ONS outputs and therefore estimates may differ.
- The project led by Nazrul Islam (University of Oxford) found that the coronavirus (COVID-19) pandemic has had a disproportionate impact on those in the most deprived areas.
- The project led by Sarah Rhodes (University of Manchester) found occupational differences in long COVID symptoms, and that occupational differences in prevalence could not be fully explained by differences in vaccine uptake, ethnicity, or viral load.
- The project led by James Munday (London School of Hygiene and Tropical Medicine) found that further improvement is needed to make more effective forecasts of COVID-19 infections.
The coronavirus (COVID-19) pandemic has had a profound impact across the UK. In response to the coronavirus pandemic, the COVID-19 Infection Survey (CIS) measures levels of infection and antibody positivity, as well as providing additional analyses covering characteristics of people testing positive, reinfections, and vaccine effectiveness.
The Office for National Statistics (ONS) announced funding awards for three academic projects on 24 December 2021, to use CIS data in innovative ways. The funding period for the projects is now complete and this article summarises methods and results for:
Coronavirus (COVID-19) and social inequalities, led by Dr Nazrul Islam, University of Oxford
Occupational analyses using the Coronavirus (COVID-19) Infection Survey, led by Sarah Rhodes, University of Manchester
Producing forecasts of COVID-19 infection by age-group in England, led by Dr James Munday, London School of Hygiene and Tropical Medicine
These analyses were produced by academics outside of the ONS. This means the methodology used differs from existing ONS outputs and therefore estimates may differ. The full academic teams are listed in Section 10, Collaboration.
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.
An odds ratio (OR) is a measure of the relative risk of an outcome in one population compared with a different population, where ORs greater than one indicate the outcome is more likely, while less than one is less likely.
Deprivation is based on an index of multiple deprivation (IMD) (PDF, 2.18MB) score or equivalent scoring method for the devolved administrations, from 1, which represents most deprived up to 100, which represents least deprived. The odds ratio shows how a 10-unit increase in deprivation score, which is equivalent to 10 percentiles or 1 decile, affects the likelihood of testing positive for COVID-19.
A measure of how often a particular event happens in one group compared with how often it happens in another group, over time. When a characteristic (for example, being male) has a hazard ratio of one, this means that there is neither an increase nor a decrease in the risk of re-infection compared with a reference category (for example, being female).
Semi-mechanistic methods are a hybrid of statistical and mechanistic models of forecasting. They use time-series dynamics and data of infectious disease dynamics to estimate a small number of epidemiological parameters under a framework which is consistent with scientific understanding of the dynamics of the system. These are used to create short term forecasting models.
The Interval Score is a Proper Scoring Rule to score quantile predictions, following Strictly proper scoring rules, prediction, and estimation, Gneiting and Raftery (2007). Smaller values are better.
Absolute Error of the Mean (AEM)
The Absolute Error of the Mean is calculated as the average error between the mean of the forecast and the true value over the n forecasts made (number of forecast dates multiplied by five days). Lower values are better.
Bias is calculated from predictive Monte-Carlo samples, automatically recognising whether forecasts are continuous or integer valued.
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.
The estimates presented in this analysis relate to self-reported long COVID, as experienced by individuals at any time, rather than clinically diagnosed ongoing symptomatic coronavirus (COVID-19) or post-COVID-19 syndrome. There is no universally agreed definition of long COVID, but it covers a broad range of symptoms such as fatigue, muscle pain and difficulty concentrating. The list of long COVID symptoms within the COVID-19 Infection Survey (CIS) can be found on our CIS questionnaires.Back to table of contents
Our Coronavirus Infection Survey (CIS) methodology article provides further information around the survey design and how we process data.
More information on the strengths and limitations of the data, data uses and users is available in our Coronavirus (COVID-19) Infection Survey QMI and our Coronavirus (COVID-19) Infection Survey statistical bulletin.
More information on the Annual Population Survey is available in the Annual population survey (APS) QMI.
Further information on the CoMix study can be found in CoMix study - Social contact survey in the UK.Back to table of contents
This project has highlighted the new ways in which the Office for National Statistics (ONS) is engaging with external experts and stakeholders. The funded part of this work has now been completed by the academics. The academics are now completing their manuscripts for publication in journals with plans for also presenting at conferences in the next year.Back to table of contents
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.
This article presents the methods and results of the three short-term, collaborative academic CIS projects funded by ONS, announced on 24 December 2021. These were led by three research teams:
COVID-19 and social Inequalities
- Dr Nazrul Islam – University of Oxford
- University of Oxford: Prof. Eva Morris, Prof. Sarah Lewington, Dr. Ben Lacey
- University of Leicester: Prof. Kamlesh Khunti, Dr. Francesco Zaccardi, Dr. Clare Gillies, Dr. Sharmin Shabnam, Dr. Cameron Razieh, Dr. Yogini Chudasama, Dr. Manish Pareek
- University of Southampton: Dr. Hajira Dambha-Miller
- ONS: Daniel Ayoubkhani, Dr. Vahe Nafilyan
- University College London: Prof. Amitava Banerjee
- Harvard University: Prof. Ichiro Kawachi
- University of Cambridge: Prof. Martin White
Occupational analyses using the ONS Coronavirus (COVID-19) Infection Survey
- Sarah Rhodes – University of Manchester
- University of Manchester: Dr. Jack Wilkinson, Dr. Matthew Gittins, Prof. Martie van Tongeren
- University of Glasgow: Dr. Evangelia Demou, Dr. Theocharis Kromydas, Prof. Srinivasa Vittal Katikireddi
- London School of Hygiene and Tropical Medicine: Prof. Neil Pearce
- University of Lancaster: Dr. Rhiannon Edge
- ONS: Dr. Vahe Nafilyan
Producing forecasts of COVID-19 infection by age-group in England
- James Munday – London School of Hygiene and Tropical Medicine
- London School of Hygiene and Tropical Medicine: Prof. Sebastian Funk
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