COVID-19 public health and social measures: a comprehensive picture of six Asian countries
Bibliographic record
Abstract
The COVID-19 pandemic will not be the last of its kind. As the world charts a way towards an equitable and resilient recovery, Public Health and Social Measures (PHSMs) that were implemented since the beginning of the pandemic need to be made a permanent feature of health systems that can be activated and readily deployed to tackle sudden surges in infections going forward. Although PHSMs aim to blunt the spread of the virus, and in turn protect lives and preserve health system capacity, there are also unintended consequences attributed to them. Importantly, the interactions between PHSMs and their accompanying key indicators that influence the strength and duration of PHSMs are elements that require in-depth exploration. This research employs case studies from six Asian countries, namely Indonesia, Singapore, South Korea, Thailand, the Philippines and Vietnam, to paint a comprehensive picture of PHSMs that protect the lives and livelihoods of populations. Nine typologies of PHSMs that emerged are as follows: (1) physical distancing, (2) border controls, (3) personal protective equipment requirements, (4) transmission monitoring, (5) surge health infrastructure capacity, (6) surge medical supplies, (7) surge human resources, (8) vaccine availability and roll-out and (9) social and economic support measures. The key indicators that influence the strength and duration of PHSMs are as follows: (1) size of community transmission, (2) number of severe cases and mortality, (3) health system capacity, (4) vaccine coverage, (5) fiscal space and (6) technology. Interactions between PHSMs can be synergistic or inhibiting, depending on various contextual factors. Fundamentally, PHSMs do not operate in silos, and a suite of PHSMs that are complementary is required to ensure that lives and livelihoods are safeguarded with an equity lens. For that to be achieved, strong governance structures and community engagement are also required at all levels of the health system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".