The impact of the social distancing policy on COVID-19 new cases in Iran: insights from an interrupted time series analysis
Why this work is in the frame
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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 20 th 2020 and April 16 th 2020. The social distancing policy was launched on March 27 th 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.001 | 0.008 |
| 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 it