The Impact of Pandemic Wave on Work Patterns and Commuting Frequency: A Retrospective Survey Analysis of COVID‐19 Data in Shanghai
Bibliographic record
Abstract
Public health crises profoundly impact work activities and commuting behaviors. The multiple waves of COVID‐19 outbreaks across cities worldwide have demonstrated that people voluntarily or involuntarily adapt their work patterns, such as shifting to remote work, and adjust their commuting choices. This study investigates changes in commuting behaviors among urban residents during three pandemic wave stages: prewave, outbreak, and postwave, focusing on work patterns, commuting frequency, and key influencing factors. A retrospective longitudinal survey was conducted in Shanghai after the first wave of COVID‐19 outbreak to collect information on respondents’ work, commuting, pandemic‐related, and sociodemographic characteristics. Descriptive analysis and statistical tests revealed a 35% increase in telecommuting and a 50% decrease in commuting trips during the outbreak, with near‐normal levels postwave. Multinomial logit models identified key factors influencing commuting frequency, such as telecommuting, weekly working hours, and direct commutes to workplaces. Telecommuting increased commuting frequency prewave, decreased it during the outbreak, and continued to reduce it postwave. Work intensity consistently increased commuting frequency, with the most significant impact prewave and the least during the outbreak. The findings provide insights for policymakers to better understand and enhance strategies in response to unforeseen public events, including potential future pandemics like Disease X.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".