Estimating transmission of avian influenza in wild birds from incomplete epizootic data: implications for surveillance and disease spread
Bibliographic record
Abstract
Summary Estimating disease transmission in wildlife populations is critical to understand host–pathogen dynamics, predict disease risks and prioritize surveillance activities. However, obtaining reliable estimates for free‐ranging populations is extremely challenging. In particular, disease surveillance programs may routinely miss the onset or end of epizootics and peak prevalence, limiting the ability to evaluate infectious processes. We used profile likelihood to estimate the force of infection ( FOI ) in a low pathogenic avian influenza virus ( LPAI v) epizootic model from censored time series of LPAI v prevalence in hatch‐year waterfowl (order Anseriformes) at postbreeding and migration sites in North America. We found a mean LPAI v FOI of 0·12 day −1 [95% CI , 0·00–0·39], corresponding to an incidence rate of 0·11 day −1 , with geographic heterogeneity (min–max: 0·02–0·23 day −1 ) among study sites. These high infection rates indicate that most hatch‐year waterfowl are likely infected with LPAI v early in the fall migration. Comparison of model‐predicted and observed immunity confirmed our assumption of naïve hatch‐year waterfowl and suggested long‐term immunity (>6 months) for adults. Using the mean LPAI v incidence rate, we predict a shorter and lower epizootic curve for highly pathogenic avian influenza virus ( HPAI v; 5 weeks with peak prevalence of 28% and 30% mortality) than LPAI v (8 weeks with peak prevalence of 50%). These findings indicate it is harder to detect HPAI v than LPAI v with swabs from live birds, which are commonly used during disease surveillance. Synthesis and applications . Our study highlights the potential of integrating incomplete surveillance data with epizootic models to quantify disease transmission and immunity. This modelling approach provides an important tool to understand spatial and temporal epizootic dynamics and inform disease surveillance. Our findings suggest focusing highly pathogenic avian influenza virus ( HPAI v) surveillance on postbreeding areas where mortality of immunologically naïve hatch‐year birds is most likely to occur, and collecting serology to enhance HPAI v detection. Our modelling approach can integrate various types of disease data facilitating its use with data from other surveillance programs (as illustrated by the estimation of infection rate during an HPAI v outbreak in mute swans Cygnus olor in Europe).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".