Exploring geographical differences and disparities of COVID-19 cases and understand the gaps in responses in South Asian countries: A three-month analysis of cases and responses
Bibliographic record
Abstract
Background and Aim: Response in a beginning of an infection is important to prevent and control any infectious disease. It has never been studied how the countries in South Asia responded in the beginning of COVID-19 infections. The aim of this study was to explore the gap in responses by geographical variations and inequalities of COVID-19 cases in South Asian Countries.Methods: Covid-19 cases, geographic and demographic data for South-Asian countries were abstracted from the news medias, Johns Hopkins University dashboard, and countries government websites. The coverage period was until May 7, 2020. Descriptive analyses of COVID-19 cases were stratified by gender and age group. Clustering and spatial analysis was performed to show the COVID-19 case distribution.Results: Over 100000 confirmed cases were found in South-Asian countries until May 7, 2020, and 95% of them are in India, Pakistan, and Bangladesh. Alarmingly, a sharp increase in new cases was observed in Bangladesh and India in early May. In this region, India reported 56% of total cases, with the highest case fatality rate of 3.4%. Approximately 70% of infected cases in this region were found in men. Approximately 42% of confirmed cases were found between the ages of 20-40, and about 20% of infected cases were found over 50 years or older. All big, economically important cities in this region were mainly infected. Bangladesh and Afghanistan reported a slow rate of recovery with 16% and 13%, respectively while India reported 29%. Afghanistan used only four tests to detect a case while India used 25 tests to detect a case showing poor numbers and insufficient test facilities in Afghanistan. Conclusion: The biggest and most economically-important cities in every South-Asian country were infected with COVID-19, where returning the migrant workers to work was a significant challenge after lifting the restrictions. Data from India, Pakistan, and Bangladesh suggest that these countries did not show the peak in the first six months. In South Asia, men were at higher risk for both infection and death, regardless of age. There were many underreported cases in these regions. Scale up services to improve the testing facilities and start a surveillance system to identify the cases rapidly especially from the marginalized population and women could reduce the burden of any infections.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".