Impact of lock down relaxation on the COVID-19 epidemic trajectory in Bangladesh
Bibliographic record
Abstract
Abstract In this projection exercise, we analyzed the circumstances of the COVID-19 pandemic in Bangladesh and used multiple methods to characterize the epidemic curve. We merged several publicly available data sets for the purpose. Projections using actual Government data as of June 16, 2020 reveals that the epidemic curve for Bangladesh may be different from that of developed countries and quite similar to such curves in countries in the region. This is true, both in terms of incidence of cases (total number of cases per million population) and length of the epidemic (months to peak or flatten the epidemic curve). We find that while Bangladesh went into lockdown early, efforts to maintain lockdown at a national level was relaxed and new cases accelerated; with significant growth happening since lifting of lockdown on May 31. Our estimates indicate prevalence of COVID-19 may be between 200,000 and 600,000 towards end of the year, may take 9 months (270 days) to flatten the epidemic curve, lifting of the lockdown may have increased total cases by 60 to 100% and may have prolonged the epidemic by additional 2-3 months.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".