An Assessment of the Smart COVID-19 Approach to Lockdown and its Empirical Evidence
Bibliographic record
Abstract
COVID-19 is a new and contagious disease that has changed human lifestyle and habits globally according to the directions provided by the World Health Organization (WHO). Until some authentic remedy or vaccine becomes available, every country is providing instructions to its public to follow precautionary measures. These measures may include lockdown, social distancing, restricting movement, and educating public about COVID-19. Lockdown is the most applied and successful way to control the virus spread and it remains helpful in curtailing the spike. However, it adversely affects developing countries like Pakistan. All types of lockdown disrupt the life of the poor and the middle class. In this paper, an intelligent-smart approach is suggested for developing countries as against complete lockdown to handle the pandemic. This approach will show the long-term results needed for controlling COVID-19 without creating any major disturbance in the economy. In this paper, evidence based approaches were used to evaluate the short-term and long-term effects of the daily increasing number of cases of COVID-19 in Pakistan. The results showed that Sindh, which has the maximum number of COVID-19 cases, is better in implementing smart lockdown as compared to other administrative regions of Pakistan. As the risk of the second wave of COVID-19 is enhanced, it would be effective to continue the intelligent-smart approach with mild SOPs to avoid the disastrous effects of COVID-19 in the future. Received Date: May 14, 2020, Last Received: December 10, 2020 Acceptance: December 25, 2020
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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 | 0.186 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".