Global spatial epidemiology of rabies: Systematic review and critical appraisal of methods
Bibliographic record
Abstract
Background: Rabies poses a profound public health burden to humans and animals worldwide in that a total of 59,000 deaths are estimated annually (95% Cl: 25–159, 00). Human deaths from rabies is set for global eradication come 2030. However, rabies transmission is geographically heterogeneous due to the complex interplay between demographic, sociocultural, economic, landscape and disease control coverage factors. Spatial and temporal epidemiological investigations can support rabies elimination efforts by enabling identification of hot spots and populations at risk, quantification of modifiable drivers and the design of risk-based surveillance. Methods and materials: We systematically reviewed published spatial epidemiological studies of rabies in both humans and animals worldwide with the view of providing a consolidated framework for the development of spatial decision support systems for rabies elimination programs locally. We used the standard systematic and meta-analysis (PRISMA) guidelines to select and review the methodological characteristics of rabies spatial epidemiological studies available in the international peer-review literature. Results: Out of 81 articles from 27 countries, 58% (47/81) studied the spatial epidemiology of rabies in animals, 26% (21/81) in both animals and humans, and 16% (13/81) in humans only. Most studies used passive surveillance data. and only six studies (7%) used time series rabies data to determine the existence of seasonal or trend patterns in the occurrence of rabies. Only 21% (17/81) of studies used spatial analytical tools to detect spatial clustering and hotspots (e.g., Average nearest neighbour, Moran's I, Getis-Ord Gi, K-function etc). While 35% (28/81) of studies modelled geographical relationships between human and/or animal rabies and various risk factors, only 9% of those generated predictive maps of the distribution of human and animal rabies. None of the spatial modelling study used all determinants of clustering for adjustment of their analyses. Conclusion: This study indicates that most rabies spatial epidemiological studies available in the literature present significant methodological deficiencies, which hamper their utility for guiding elimination efforts. The identified deficiencies are as result of poor data quality, model building approaches and the selection of predictors. We present a general framework to the application of medical geography methods to uncover areas at higher risk of rabies transmission to aid local rabies surveillance and control.
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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 | 0.068 | 0.241 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.019 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".