Developing a Dynamic Social Vulnerability Index for Public Health (DSVI-PH) Using Location-based Data
Bibliographic record
Abstract
This study developed a Dynamic Social Vulnerability Index for Public Health (DSVI-PH) by integrating the CDC’s social vulnerability index with Meta population movement data and hospital location datasets. We chose Hurricane Beryl (2024) as a target disaster and captured daily changes in vulnerability through Meta Data for Good Movement data, which is unlike static annual social vulnerability index data. Summary of Findings DSVI-PH captured daily fluctuations in social vulnerability by evacuation, return migration, and hospital accessibility compared to the current static social vulnerability index. Hot and cold spot analysis revealed that spatial disparities within major cities (Houston, San Antonio, and Corpus Christi) influenced the distribution of vulnerability. Policy and Practice Implications Policymakers can use DSVI-PH to guide real-time allocation of resources, prioritizing highly vulnerable populations. DSVI-PH provides a quantitative change of latent construct (social vulnerability), so we can set up the quantified adaptive recovery policies.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".