Estimating transmission of avian influenza in wild birds from incomplete epizootic data: implications for surveillance and disease spread

Estimating disease transmission in wildlife populations is critical to understand hostpathogen 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 (LPAIv) epizootic model from censored time series of LPAIv prevalence in hatch-year waterfowl (order Anseriformes) at postbreeding and migration sites in North America. We found a mean LPAIv FOI of 0 center dot 12day1 [95% CI, 0 center dot 000 center dot 39], corresponding to an incidence rate of 0 center dot 11day1, with geographic heterogeneity (minmax: 0 center dot 020 center dot 23day1) among study sites. These high infection rates indicate that most hatch-year waterfowl are likely infected with LPAIv early in the fall migration. Comparison of model-predicted and observed immunity confirmed our assumption of naive hatch-year waterfowl and suggested long-term immunity (>6months) for adults. Using the mean LPAIv incidence rate, we predict a shorter and lower epizootic curve for highly pathogenic avian influenza virus (HPAIv; 5weeks with peak prevalence of 28% and 30% mortality) than LPAIv (8weeks with peak prevalence of 50%). These findings indicate it is harder to detect HPAIv than LPAIv 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 (HPAIv) surveillance on postbreeding areas where mortality of immunologically naive hatch-year birds is most likely to occur, and collecting serology to enhance HPAIv 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 HPAIv outbreak in mute swans Cygnus olor in Europe).