Social contact for daily activities during the COVID-19 pandemic in six developed countries
Bibliographic record
Abstract
Social contact serves as a pivotal factor in the transmission dynamics of SARS-CoV-2, the virus responsible for COVID-19. Thus, comprehending the shifts in social contact patterns during the pandemic is paramount. This chapter offers a comprehensive overview of studies examining social contacts within the context of the pandemic. It introduces a novel social contact survey conducted across six developed nations—Australia, Canada, Japan, New Zealand, the United Kingdom, the United States—spanning from March to May 2021. The data facilitates the analysis of changes in individuals’ social contact behaviors between the pre-pandemic influenza season and the during-pandemic period across eight distinct contact settings. It not only calculates the total number of social contacts for all participants but also computes the mean number of social contacts for individuals maintaining their usual level of interactions during the pandemic. Moreover, this chapter reveals the diversity in social contacts across various personal attributes and settings, providing valuable insights into nuanced contact patterns. Ultimately, this chapter identifies contexts with heightened infection risks through social contacts and proposes targeted interventions for these settings. The findings presented herein serve as a foundation for future research on social contacts and furnish scientific evidence to guide policymaking in the post-pandemic landscape.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".