SEEDNet: Covariate-free multi-country settlement-level epidemiological estimates datasets for network analysis
Bibliographic record
Abstract
ABSTRACT The study of population health through network science holds high promise, but data sources that allow complete representation of populations are limited in low-and middle-income countries. Large national health surveys designed to gather nationally representative health and development data are promising data sources but are not designed to produce small-area estimates of health indicators. Methods for producing these from national surveys tend to rely on varied covariate data sources and are computationally demanding, limiting their use for network representations of populations. To reduce the sources of measurement error and allow efficient multi-country representation of populations as networks of human settlements here, we present SEEDNet (Settlement-level Epidemiological Estimates Datasets for Network Analysis) 1 , a data library of multi-country representations of population health across human settlements. Our covariate-free method uses georeferenced national surveys to produce SAEs of health indicators through local inverse-distance weighted interpolation and includes an algorithm for the comprehensive identification of population settlements of all sizes across the globe. Our estimates are cross-validated against those obtained using a Bayesian Geostatistical Model. The method is fully automated, requiring a single standard georeferenced survey data source for mapping populations, eliminating the need for indicator or country-specific covariate selection by investigators. Computational efficiency is achieved by restricting computation to human-occupied areas and by adopting a logical aggregation of estimates into the complete range of settlement sizes. Standardized georeferenced settlement-level datasets for 15 indicators and 10 countries were validated in this paper, as well as the novel method to identify settlements. SEEDNet 1 is a specialized library of nodes that can serve as a basis for network representations of population health in low-and middle-income countries.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".