Associations Between Environmental Conditions and Infection With Respiratory Syncytial Virus in Japan: A Spatiotemporal Analysis
Bibliographic record
Abstract
Background: Respiratory syncytial virus (RSV) poses a significant disease burden among children <5 worldwide. Yet systematic analyses of how complex environmental factors are associated with RSV transmission are still lacking in many countries. Methods: We introduced a novel 3-stage, data-driven framework to assess the impacts of environmental factors, including meteorological conditions, air pollutants, and extreme weather, on RSV infections from a spatiotemporal perspective. It includes (1) spatiotemporal patterns of RSV transmission; (2) a hierarchical model (HSDLNM) to examine associations between environmental factors and RSV transmission, estimating relative risks (RRs) and 95% confidence intervals; and (3) an interpretable machine learning model, Gaussian Process Boosting, to predict RSV infections using historical environmental data. We validated the applicability of the proposed framework in Japan. Results: is associated with elevated infection risk. Additionally, historical environmental data aid in forecasting RSV activities in Japan. Conclusions: This study presents the relationships between environmental factors and RSV infections in Japan. Our framework could be applied to areas with similar RSV seasonality to further understand environmental impacts regionally. This research helps inform policy decisions on RSV prophylaxis strategies, supporting cost-effective measures for controlling and preventing early transmission.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".