Social behaviours and contact patterns across the 2020/21, 2021/22 and 2022/23 winter seasons in the UK, and associations with symptoms
Bibliographic record
Abstract
Abstract The SARS-CoV-2 pandemic had a large impact on social mixing in the UK. This study analysed data from the Office for National Statistics Coronavirus Infection Survey to examine changes in contact patterns and self-reported symptoms through three winter seasons from 2020 to 2023. Using Generalised Additive Models, we estimated levels of various contacts over time, accounting for age, sex, ethnicity, and deprivation percentile, and compared these to trends in self-reported symptoms. Our estimates indicated steady increases in physical contacts from quarter-4 2020 to the end of the study in quarter-1 2023, with notable variation in age-specific trends. School closures and holiday periods had substantial impacts on contact patterns, particularly for children. Prevalence of reported symptoms also increased steadily over time, but varied much more within-season than most contacts; specifically, the relative increase in respiratory symptom prevalence during winter peaks between seasons was much larger than increases in contacts. Our estimates also suggested that while age played a crucial role in both contact patterns and symptom reporting, the effects of deprivation were less clear and far smaller. Our findings provide insights into changes in behaviours and symptoms during the pandemic, which may help inform future public health policy and infection modelling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".