An Overview on COVID-19 Outbreaks Scenario in South Asia
Why this work is in the frame
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Bibliographic record
Abstract
The global emergence of the recently discovered COVID-19 (Coronavirus disease 2019; SARS-CoV-2) has already shown its devastating effects on almost the entire world by causing huge numbers of death cases and rupturing the whole economy as well as social communication. South Asia, a region that comprises mostly of least developed and developing countries (Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka) with overpopulation, illiteracy, poverty, lack of awareness, lack of hygiene, inadequate health care facilities, is still struggling to fight against this virus and facing the consequences with over 8.5 million confirmed cases including 130,636 deaths till the 20th October. Prompt and proper protective measures, good health care systems, and conscious people are the keys to reducing the severe impacts of this pandemic situation, and most of the countries in this region lack all of this. Considering this, it will not be a surprise if the pandemic takes its full shape in these countries and recent evidence also suggest that the situation is already on its way to reach the peak. However, the pandemic nature in South Asia also demonstrates that strict measures by the government and co-operation from the people can protect a country from the impacts of the virus, whereas lack of these can lead to the next heat point. This review demonstrates and compares the impact of COVID-19 in the mass population of South Asia which could support the government and scientific community to take proper protective measures against this global pandemic and better prepare the community for future challenges. Moreover, good health care systems, public health infrastructure, and up to date information on COVID-19 outbreaks in this region will help to combat this pandemic and create more sustainable and resilient healthy societies in South Asia.
 Bioresearch Commu. 7(1): 973-981, 2021 (January)
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 it