Seasonality of Human Influenza and Co-Seasonality with Avian Influenza in Bangladesh, 2010-2019
Bibliographic record
Abstract
Purpose: Seasonality and periodicity of influenza in human and animal populations is not well characterized in tropical and subtropical regions globally. In Bangladesh, where over 90% of poultry products are marketed through live poultry markets, avian influenza is prevalent in poultry and epidemics of influenza occur in humans. We sought to empirically examine the timing and intensity of influenza seasonality in humans and avian influenza in poultry in Bangladesh using time series methods. Methods & Materials: We evaluated 8,790 cases of human influenza occurring between January 2010 and December 2019 obtained from 32 hospital-based sentinel surveillance sites across Bangladesh. Avian influenza positive samples (n=2,274) were obtained from environmental surveillance across 106 live poultry markets in Dhaka between January 2016 and December 2019. We applied wavelet analysis to determine influenza periodicity, and conducted Poisson regression with harmonic terms adjusting for trend and an offset for samples tested to estimate the average timing and intensity of the primary influenza peak in each population. We examine co-seasonality by estimating the time lag between the annual influenza peak in humans and the annual avian influenza peak in poultry. Results: Over the 10-year period there was consistent annual periodicity of influenza in humans with peak incidence occurring in early July (peak calendar-week: 27.3, 95%CI: 27.0-27.5) and intense seasonality (amplitude: 14.7, 95%CI: 13.4-16.2). Large metropolitan centers displayed earlier epidemic timing, with epidemic spread following a spatial diffusion pattern based on geographic proximity. Comparatively, avian influenza displayed weak seasonality, with moderate year-round transmission and only a small peak in late March to early April (peak calendar-week: 13.5, 95%CI: 11.5-16.8; amplitude: 1.5, 95%CI: 1.3-1.7). Peak avian influenza preceded peak human influenza by 13.8 calendar-weeks (95%CI: 10.7-15.5). Conclusion: Our findings suggest that while influenza epidemics in humans are highly seasonal and take off faster in metropolitan areas, avian influenza in poultry does not display a strong seasonal trend in Bangladesh. Although influenza peaks do not coincide, endemic avian influenza continues to pose a risk for viral reassortment throughout the year. These co-seasonality results provide evidence for more quantitative risk assessments of viral reassortment and inform timing of sequencing-based surveillance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".