Continuing to be cautious: Japanese contact patterns during the COVID-19 pandemic and their association with public health recommendations
Bibliographic record
Abstract
Despite implementing no lockdowns and having a large elderly population, Japan had a low mortality rate due to COVID-19 compared to Europe and North America. The extent to which policies impacted person-to-person contact remains unclear. In this study, we examined changes in contact patterns and their association with behaviors and governmental recommendations in Japan during the pandemic. Ten social contact surveys were conducted between 2021 and 2023 reaching over 1500 participants per survey in Osaka and Fukuoka prefectures where governmental recommendations were first implemented due to high COVID-19 incidence. Their contact patterns were assessed through their demographic characteristics, COVID-19 vaccination status, and individual disease mitigation measures. Generalized linear models were used to identify factors associated with increased contacts. The mean number of contacts during the pandemic declined by at least 49.8% (8.2 weekday contacts and 6.0 weekend contacts per individual, adjusted by age and sex) compared to a study conducted prior to 2020. Weekdays, occupation, larger household sizes, and mask wearing were associated with a higher number of contacts. The frequency and duration of contacts were negatively associated with the issuance of COVID-19 governmental measures, yet the relative change in contacts was not as prominent as pre- and post-lockdown situations in the United Kingdom. There was a gradual increase in contacts with time and less strict public health recommendations. Yet, contacts that did not increase with uptake of COVID-19 vaccination and continuous mask wearing depict cautious behavior across the survey population during the pandemic and into 2023. These results are in contrast with European countries where contacts largely increased among vaccinated individuals compared to the non-vaccinated. Social contacts are country and context specific, highlighting the need for data collection across different communities.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".