COVID-19 pandemic hazard–risk–vulnerability analysis: a framework for an effective Pan-India response
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study evaluated the COVID-19 risk considering positive cases (as a hazard) and the demographic structure (as a socio-economic vulnerability) at districts scale across India using fuzzy analytical hierarchical process and geospatial modelling. Despite the fact that the high and very high COVID-19 hazard was observed in a limited area (14.2%, 233 districts), the proportion of high to very high COVID-19 risk was evident in larger regions (42.5%, 575 districts). A moderate to very high socio-economic vulnerability was recorded in major parts of the country (60.0%, 557 districts), while the districts with megacities had been severely affected due to the more complex urban and social systems. The study highlights the zones under high COVID-19 hazard and its possible linkages with vulnerability and risk at district scales in India that may effectively support emergency preparedness and response mechanisms during the different waves of the pandemic.
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.
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 | 0.230 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it