RETRACTED: COVID-19 in Bangladesh: Wave-centric assessments and mitigation measures for future pandemics
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
The ongoing pandemic COVID-19 caused by Severe Acute Respiratory Coronavirus-2 (SARS-CoV-2) has wreaked havoc globally by affecting millions of lives. Although different countries found the implementation of emergency measures useful to combat the viral pandemic, many countries are still experiencing the resurgence of COVID-19 cases with new variants even after following strict containment guidelines. Country-specific lessons learned from the ongoing COVID-19 pandemic can be utilized in commencing a successful battle against the potential future outbreaks. In this article, we analyzed the overall scenario of the COVID-19 pandemic in Bangladesh from Alpha to Omicron variant and discussed the demographic, political, economic, social, and environmental influences on the mitigation strategies employed by the country to combat the pandemic. We also tried to explore the preparedness and precautionary measures taken by the responsible authorities, the choice of strategies implemented, and the effectiveness of the response initiated by the government and relevant agencies. Finally, we discussed the possible strategies that might help Bangladesh to combat future COVID-19 waves and other possible pandemics based on the experiences gathered from the ongoing COVID-19 pandemic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".