Stronger together: a new pandemic agenda for South Asia
Bibliographic record
Abstract
The global increase in COVID-19 cases in 2021 has primarily been due to an uncontrolled surge in South Asia. It is estimated that by 1 September 2021, approximately 1.4 million in South Asians will die due to COVID-19 alone.1 The total number of excess deaths will be much higher—including non-COVID causes, as health systems are on the brink of collapse.2 With 33.4% of South Asians being extremely poor3 and the large-scale loss of livelihood being reported, the region faces a potentially catastrophic future for the ongoing decade.4 However, countries in South Asia continue to remain divisive. This differs from other geographic ‘blocs’ that frequently cooperate on mutual interest issues.5 Tensions in South Asia are shaped by complex domestic, bilateral, intra-regional and international geopolitical factors, despite the region’s obvious geographic, economic and cultural interdependence. A key lesson from the current pandemic is that countries need to share lessons and actively coordinate, complement and supplement each other’s public health responses, especially between neighbours. We present a pragmatic ‘Stronger Together’ agenda (table 1) on critical areas of concern for political, social, medical and public health leaders in South Asia to consider and build on. View this table: Table 1 Key recommendations of a new ‘Stronger Together’ pandemic agenda for South Asia The uncontrolled spread of COVID-19 in many parts of South Asia implies that newer variants will continue to emerge. Some variants will inherently display increased transmissibility, infectivity and vaccine/antigenic escape capability, making it difficult for us to track and intelligently act on them.6 Rapidly scaling up capacity for genomics and rolling out …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".