The effect of meteorological factors on severe fever with thrombocytopenia syndrome: Evidence from 34 Chinese cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Severe fever with thrombocytopenia syndrome (SFTS) is a climate-sensitive infectious disease, and its spatial distribution has been expanding in recent years. This study aimed to investigate the influence of meteorological factors on SFTS incidence. Methods: Data on SFTS was extracted from the Infectious Disease Surveillance Report Management System from January 1, 2011 to December 31, 2023. A two-stage hierarchical analytical framework was employed in this study. First, a distributed lag nonlinear model was utilized to characterize the nonlinear exposure-response relationships between meteorological factors and the incidence of SFTS at the municipal level. Second, a multivariate meta-analysis was conducted to synthesize city-specific effect estimates, with explicit adjustment for inter-regional heterogeneity. Results: : 0.10-0.80). Conclusions: This study demonstrates that temperature and precipitation significantly influence SFTS incidence, with effects lagging consistently by 1-2 months. These findings can be integrated into China's Smart Multi-Point Surveillance System by incorporating region-specific meteorological thresholds to trigger early warnings. The system could then activate targeted interventions, such as tick control measures, accounting for the observed 1-2 month lag between climatic conditions and disease occurrence. Such climate-adaptive approaches would enhance the precision and timeliness of SFTS prevention and control efforts nationwide.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it