Item talk:Q244811
From geokb
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"name": "Estimating transmission of avian influenza in wild birds from incomplete epizootic data: implications for surveillance and disease spreac",
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"abstract": "\nEstimating disease transmission in wildlife populations is critical to understand host\u2013pathogen 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.\nWe 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.\nWe found a mean LPAIv FOI of 0\u00b712\u00a0day\u22121\u00a0[95% CI, 0\u00b700\u20130\u00b739], corresponding to an incidence rate of 0\u00b711\u00a0day\u22121, with geographic heterogeneity (min\u2013max: 0\u00b702\u20130\u00b723\u00a0day\u22121) among study sites. These high infection rates indicate that most hatch-year waterfowl are likely infected with LPAIv early in the fall migration.\nComparison of model-predicted and observed immunity confirmed our assumption of na\u00efve hatch-year waterfowl and suggested long-term immunity (>6\u00a0months) for adults.\nUsing the mean LPAIv incidence rate, we predict a shorter and lower epizootic curve for highly pathogenic avian influenza virus (HPAIv; 5\u00a0weeks with peak prevalence of 28% and 30% mortality) than LPAIv (8\u00a0weeks 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.\nSynthesis 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 na\u00efve 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 swansCygnus olor\u00a0in Europe).\n",
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