Characterisation of the respiratory syncytial virus seasonality and its environmental factors in the Americas—a multi-country observational study using routine surveillance networks
Bibliographic record
Abstract
Background: Respiratory Syncytial Virus (RSV) is an important cause of bronchiolitis and pneumonia in young children. Circulation patterns represent challenges for immunoprophylaxis, requiring tailored interventions to address RSV activity linked to climate. We assessed RSV seasonality across the Americas and its relation to environmental factors and influenza circulation. Methods: RSV seasonality was assessed using data reported in 2010-2019 to a multi-country respiratory surveillance network. Time-series analysis identified temporal patterns and trends. Negative binomial, Moving Epidemics Method, and WHO Moving Averages Models were compared to assess seasonality. Correlation and regression were used for associations of RSV with environmental and influenza predictors. Findings: During 2010-2019, 32 countries in the Americas reported 14,308,503 respiratory samples, with 446,648 RSV-positive (3.12%) samples. RSV seasonal epidemics progressed from south to north. In South America, RSV seasons began in early May, peaking in August. RSV seasonality was less distinct in Caribbean; RSV started in September and peaked in October-November. Central Americas' RSV season lagged behind influenza, whereas in the Andes, it peaked earlier. At higher latitudes, RSV epidemics occurred earlier with shorter durations. RSV circulation negatively correlated with lower temperatures (-0.43; p < 0.0001), and precipitation (-0.04; p = 0.0035); and was positively correlated with decreased longitude (0.12; p < 0.0001) and barometric pressure (0.15; p < 0.0001), and was associated with lower elevation (0.02; p = 0.10), and westerly locations (0.12; p < 0.0001). Interpretation: Subregional and interannual variations in RSV seasonality were influenced by environmental factors, underscoring the importance of ongoing surveillance. Collaborative efforts improve surveillance, shaping evidence-based strategies for preventive product introductions and effective RSV control. Funding: The publication of this work was supported by the United States Centers for Disease Control and Prevention through a cooperative agreement with the Pan American Health Organization/World Health Organization.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".