Quantifying the impacts of human mobility restriction on the spread of COVID-19: an empirical analysis from 344 cities of China
Bibliographic record
Abstract
Abstract Objective Since the outbreak of novel coronavirus pneumonia (COVID-19), human mobility restriction measures have raised controversies, partly due to inconsistent findings. Empirical study is urgently needed to reliably assess the causal effects of mobility restriction. Methods Our study applied the difference-in-difference (DID) model to assess declines of population mobility at the city level, and used the log-log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time, after adjusting for confounders. Results The DID model showed that a continual expansion of the relative declines over time in 2020. After four weeks, population mobility declined by 54.81% (interquartile ranges, −65.50% to −43.56%). The accrued population mobility declines were associated with significant reduction of cumulative COVID-19 cases throughout six weeks (i.e., 1% decline of population mobility was associated with 0.72% (95%CI 0.50% to 0.93%) reduce of cumulative cases for one week, 1.42% two weeks, 1.69% three weeks, 1.72% four weeks,1.64% five weeks and 1.52% six weeks). The impact on weekly new cases seemed greater in the first four weeks, but faded thereafter. The effects on cumulative cases differed by cities of different population sizes, with greater effects seen in larger cities. Conclusion Persistent population mobility restrictions are well deserved. However, a change in the degree of mobility restriction may be warranted over time, particularly after several weeks of rigorous mobility restriction. Implementation of mobility restrictions in major cities with large population sizes may be even more important.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".