Interrupted time series analysis of the implementation of social distancing policy, its lifting and the mandate of wearing face masks in Iran to mitigate against COVID-19
Bibliographic record
Abstract
Abstract BackgroundCOVID-19 was first reported in Wuhan, China, and has spread rapidly around the world. The purpose of this study was to investigate the effects of implementing social distancing policy, and the impact of its lifting, with the resumption of social contacts and activities, as well as the effects of mandating face masks on the temporal trend of new COVID-19 cases in Iran. Methods We employed the interrupted time series analysis (ITSA), which is a very valuable method that can be used to evaluate the impact of the implementation of various policies in the health sector to help health policy-makers make effective decisions. Daily data were collected from the Ministry of Health and Medical Education and the World Health Organization from 954 public hospitals and health center settings. Data were extracted 14 days before and after the implementation of each policy. Results were computed with their 95% confidence interval (CI) and p-values equal to or less than 0.05 were considered as statistically significant. All data were analyzed using the open-source software R Version 3.6.1 using the “nlme” and “car” packages.ResultsThe slope of changes in new confirmed cases following the implementation of the social distancing policy decreased by 118.79 (P <0.001). With the resumption of social and economic activities in all provinces except for Tehran, initially the number of new daily confirmed cases was 3300, which was statistically significant (P <0.001). The slope of changes due to the implementation of this policy was 47.89 (P <0.001). A similar trend was detected with the resumption of social and economic activities in Tehran. With the implementation of the policy of mandatory use of masks, the slope of changes showed a decrease of 25.84 (P <0.001). Conclusion Given the absence of effective drugs and vaccines against COVID-19, policy-makers have implemented non-pharmacological interventions to reduce the transmission of the disease and prevent more deaths. Social distancing may be unsustainable in the long-term, while wearing masks is both a cost-effective and efficacious measure to curb disease transmission.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".