Seasonality of influenza and coseasonality with avian influenza in Bangladesh, 2010–19: a retrospective, time-series analysis
Bibliographic record
Abstract
BACKGROUND: Seasonal and avian influenza viruses circulate among human and poultry populations in Bangladesh. However, the epidemiology of influenza is not well defined in this setting. We aimed to characterise influenza seasonality, examine regional heterogeneity in transmission, and evaluate coseasonality between circulating influenza viruses in Bangladesh. METHODS: In this retrospective, time-series study, we used data collected between January, 2010, and December, 2019, from 32 hospital-based influenza surveillance sites across Bangladesh. We estimated influenza peak timing and intensity in ten regions using negative binomial harmonic regression models, and applied meta-analytic methods to determine whether seasonality differed across regions. Using live bird market surveillance data in Dhaka, Bangladesh, we estimated avian influenza seasonality and examined coseasonality between human and avian influenza viruses. FINDINGS: =93·9%; p<0·0001), with metropolitan regions peaking earlier and epidemic spread following a spatial diffusion pattern based on geographical proximity. Comparatively, avian influenza displayed weak seasonality, with moderate year-round transmission and a small peak in April (peak calendar week 14·9, 95% CI 13·2-17·0), which was out of phase with influenza peaks in humans. INTERPRETATION: In Bangladesh, influenza prevention and control activities could be timed with annual seasonality, and regional heterogeneity should be considered in health resource planning. Year-round avian influenza transmission poses a risk for viral spillover, and targeted efforts will be crucial for mitigating potential reassortment and future pandemic threats. FUNDING: Canadian Institute of Health Research Vanier Canada Graduate Scholarship.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".