The impact of the social distancing policy on COVID-19 new cases in Iran: insights from an interrupted time series analysis
Bibliographic record
Abstract
Abstract Background In late December 2019, a viral outbreak occurred in Wuhan, province of Hubei, People’s Republic of China, and rapidly spread out worldwide. The infectious agent was identified and termed as SARS-CoV-2, responsible of the “coronavirus disease 19” (COVID-19). Due to the lack of vaccines and effective drugs for this disease, many policy- and decision-makers have focused on non-pharmacological methods to prevent and control this disease. Social distancing can be effective in reducing the spread of the outbreak. This study was aimed at assessing the effects of the implementation of the social distancing policy in Iran, one of the countries most affected by the COVID-19. Methods This study was designed as a quasi-experimental study, and was conducted utilizing the interrupted time series analysis (ITSA) approach. Daily data was collected between February 20th 2020 and April 16th 2020. The social distancing policy was launched on March 27th 2020.Results A significant decrease of -288.57 (95% CI: 269.08 (95% CI: -83.37 to -621.55, P-value=0.04) new confirmed cases following the implementation of the social distancing policy was found, corresponding to a daily decrease in the trend of -8.10 (95% CI: -10.02 to -6.19, P-value=0.001). A significant decrease of -24.78 (95% CI: -42.97 to -6.58, P-value=0.01) new deaths following the implementation of the social distancing policy could be found, corresponding to a daily decrease in the trend of -8.10 (95% CI: -10.02 to -6.19, P-value=0.001). Conclusion The growth rate of new cases and deaths from the COVID-19 in Iran has significantly decreased after the implementation of social distancing. By monitoring and implementing this policy in all countries, the burden of COVID-19 can be mitigated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".