Lessons and Challenges to be learned from different countries policy implication on COVID 19 recovery cases- A cross-sectional descriptive study
Bibliographic record
Abstract
Abstract Background- The need to quantify the non-pharmaceutical measures in policy decision making is essential in current uncertain times of pandemic. The purpose of the current study is to quantify the relationship between Social Distancing measures and the Total number of tests performed with the Total number of recovered cases across 23 countries around the world, currently struck by COVID-19 pandemic.Methods- The cross-sectional descriptive study utilized STATA 16. for Poisson Model analysis using data collected across 23 countries. The statistical databases Statista, WHO situation reports, CDC website, respective country health ministry websites, and World Bank data was utilized to collected the lacking data details regarding COVID-19. The WHO regions/23 countries included in analysis are Republic of Korea, Japan, Australia, Malaysia, Philippines, Thailand, India, United States of America, Canada, Italy,Germany,United Kingdom,France,Austria,Croatia,Israel,Russian Federation,Spain,Belgium,Finland,Sweden,Switzerland,Iran (Islamic Republic of). The variables included in analysis are The factorial analysis of categorical data is included to quantify the levels of social distancing measures and its effect on the total number of recovered cases until April 2nd, 2020. Results- There exists a positive relationship between the improved number of recovered infected cases, and Social distancing measures of lockdown, the total number of tests performed depending on the stage at which it is completed. The availability of total medical doctors in each country affects the number of recovered cases in that particular country. Conclusion- Future studies might use it as a foundation for evaluation modeling in public health for policy decision making.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".