Seasonal Dynamics of Influenza and RSV in the Caribbean: A Call for Regionally Tailored Preventive Measures
Bibliographic record
Abstract
Abstract Introduction Respiratory tract infections (RTIs) remain a leading global cause of morbidity and mortality, with the Caribbean reporting some of the highest incidence rates. The World Health Organization recommends tailoring prevention strategies to local viral epidemiology. We aim to characterize the seasonal trends and disease burden of major respiratory viruses in the Caribbean region of the Kingdom of the Netherlands. Methods We conducted a retrospective observational study using virological surveillance data routinely collected between 2018 and 2024 from Aruba, Bonaire, Curaçao, Sint Maarten, Saba, and Sint Eustatius. Seasonal patterns of rhinovirus, influenza virus and respiratory syncytial viruses (RSV) were modelled using generalised additive models. Associations with climate, tourism, age, and disease severity were assessed with generalised linear models. Results Rhinovirus was the most frequently detected virus across all islands. Influenza virus peaked between November and March (p < 0.001), aligning with seasonal trends in the Northern Hemisphere and coinciding with the high tourism season in Aruba (OR = 8.72; 95% CI: 6.37–12.10), Curaçao (OR = 3.05; 95% CI: 1.59–6.16), and Sint Maarten (OR = 10.83; 95% CI: 2.13–198.83). In contrast, RSV activity peaked from June to December (p < 0.001), corresponding with the rainy season in Aruba (OR = 6.42; 95% CI: 4.26–9.75), and Sint Maarten (OR = 7.27; 95% CI: 2.31–28.22). Rhinovirus detection was significantly associated with increased disease severity, including the need for oxygen therapy (OR = 2.78; 95% CI: 1.72-4.50) and presentation with dyspnoea or tachypnoea (OR = 2.26; 95% CI: 1.42-3.67). Conclusions RSV seasonality in the Caribbean aligns with the rainy season and diverged from patterns in the Netherlands, indicating that current European-based intervention schedules may not be optimally timed. By integrating virological surveillance data from all six islands, this study offers a unique regional perspective to inform public health policy.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".