Who Counts Where? COVID-19 Surveillance in Federal Countries
Bibliographic record
Abstract
CONTEXT: While the World Health Organization (WHO) has established guidance on COVID-19 surveillance, little is known about implementation of these guidelines in federations, which fragment authority across multiple levels of government. This study examines how subnational governments in federal democracies collect and report data on COVID-19 cases and mortality associated with COVID-19. METHODS: We collected data from subnational government websites in 15 federal democracies to construct indices of COVID-19 data quality. Using bivariate and multivariate regression, we analyzed the relationship between these indices and indicators of state capacity, the decentralization of resources and authority, and the quality of democratic institutions. We supplement these quantitative analyses with qualitative case studies of subnational COVID-19 data in Brazil, Spain, and the United States. FINDINGS: Subnational governments in federations vary in their collection of data on COVID-19 mortality, testing, hospitalization, and demographics. There are statistically significant associations (p < 0.05) between subnational data quality and key indicators of public health system capacity, fiscal decentralization, and the quality of democratic institutions. Case studies illustrate the importance of both governmental and civil-society institutions that foster accountability. CONCLUSIONS: The quality of subnational COVID-19 surveillance data in federations depends in part on public health system capacity, fiscal decentralization, and the quality of democracy.
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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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".