Quantitative transmission characteristics of different H5 low pathogenic avian influenza viruses in Muscovy ducks

Are all avian influenza outbreaks in poultry the same? The predicted impact of poultry species and virus subtype

AIMS

Outbreaks of avian influenza in poultry farms are currently increasing in frequency, with devastating consequences for animal welfare, farmers and supply chains. Some studies have documented the direct spread of the avian influenza virus between farms. Prevention of spread between farms relies on biosecurity surveillance and control measures. However, the evolution of an outbreak on a farm might vary depending on the virus strain and poultry species involved; this would have important implications for surveillance systems, epidemiological investigations and control measures.

METHODS AND RESULTS

In this study, we utilized existing parameter estimates from the literature to evaluate the predicted course of an epidemic in a standard poultry flock with 10,000 birds. We used a stochastic SEIR simulation model to simulate outbreaks in different species and with different virus subtypes. The simulations predicted large differences in the duration and severity of outbreaks, depending on the virus subtypes. For both turkeys and chickens, outbreaks with HPAI were of shorter duration than outbreaks with LPAI. In outbreaks involving the infection of chickens with different virus subtypes, the shortest epidemic involved H7N7 and HPAIV H5N1 (median duration of 9 and 17 days, respectively) and the longest involved H5N2 (median duration of 68 days). The most severe outbreaks (number of chickens infected) were predicted for H5N1, H7N1 and H7N3 virus subtypes, and the least severe for H5N2 and H7N7, in which outbreaks for the latter subtype were predicted to develop most slowly.

CONCLUSIONS

These simulation results suggest that surveillance of certain subtypes of avian influenza virus, in chicken flocks in particular, needs to be sensitive and timely if infection is to be detected with sufficient time to implement control measures. The variability in the predictions highlights that avian influenza outbreaks are different in severity, speed and duration, so surveillance and disease response need to be nuanced and fit the specific context of poultry species and virus subtypes.

Using surveillance data for early warning modelling of highly pathogenic avian influenza in Europe reveals a seasonal shift in transmission, 2016-2022

Avian influenza in wild birds and poultry flocks constitutes a problem for animal welfare, food security and public health. In recent years there have been increasing numbers of outbreaks in Europe, with many poultry flocks culled after being infected with highly pathogenic avian influenza (HPAI). Continuous monitoring is crucial to enable timely implementation of control to prevent HPAI spread from wild birds to poultry and between poultry flocks within a country. We here utilize readily available public surveillance data and time-series models to predict HPAI detections within European countries and show a seasonal shift that happened during 2021-2022. The output is models capable of monitoring the weekly risk of HPAI outbreaks, to support decision making.

Predictive analysis for pathogenicity classification of H5Nx avian influenza strains using machine learning techniques

Over the past decades, avian influenza (AI) outbreaks have been reported across different parts of the globe, resulting in large-scale economic and livestock loss and, in some cases raising concerns about their zoonotic potential. The virulence and pathogenicity of H5Nx (e.g., H5N1, H5N2) AI strains for poultry could be inferred through various approaches, and it has been frequently performed by detecting certain pathogenicity markers in their haemagglutinin (HA) gene. The utilization of predictive modeling methods represents a possible approach to exploring this genotypic-phenotypic relationship for assisting experts in determining the pathogenicity of circulating AI viruses. Therefore, the main objective of this study was to evaluate the predictive performance of different machine learning (ML) techniques for in-silico prediction of pathogenicity of H5Nx viruses in poultry, using complete genetic sequences of the HA gene. We annotated 2137 H5Nx HA gene sequences based on the presence of the polybasic HA cleavage site (HACS) with 46.33% and 53.67% of sequences previously identified as highly pathogenic (HP) and low pathogenic (LP), respectively. We compared the performance of different ML classifiers (e.g., logistic regression (LR) with the lasso and ridge regularization, random forest (RF), K-nearest neighbor (KNN), Naïve Bayes (NB), support vector machine (SVM), and convolutional neural network (CNN)) for pathogenicity classification of raw H5Nx nucleotide and protein sequences using a 10-fold cross-validation technique. We found that different ML techniques can be successfully used for the pathogenicity classification of H5 sequences with ∼99% classification accuracy. Our results indicate that for pathogenicity classification of (1) aligned deoxyribonucleic acid (DNA) and protein sequences, with NB classifier had the lowest accuracies of 98.41% (+/-0.89) and 98.31% (+/-1.06), respectively; (2) aligned DNA and protein sequences, with LR (L1/L2), KNN, SVM (radial basis function (RBF)) and CNN classifiers had the highest accuracies of 99.20% (+/-0.54) and 99.20% (+/-0.38), respectively; (3) unaligned DNA and protein sequences, with CNN's achieved accuracies of 98.54% (+/-0.68) and 99.20% (+/-0.50), respectively. ML methods show potential for regular classification of H5Nx virus pathogenicity for poultry species, particularly when sequences containing regular markers were frequently present in the training dataset.

A review of estimated transmission parameters for the spread of avian influenza viruses

Avian influenza poses an increasing problem in Europe and around the world. Simulation models are a useful tool to predict the spatiotemporal risk of avian influenza spread and evaluate appropriate control actions. To develop realistic simulation models, valid transmission parameters are critical. Here, we reviewed published estimates of the basic reproduction number (R₀ ), the latent period and the infectious period by virus type, pathogenicity, species, study type and poultry flock unit. We found a large variation in the parameter estimates, with highest R₀ estimates for H5N1 and H7N3 compared with other types; for low pathogenic avian influenza compared with high pathogenic avian influenza types; for ducks compared with other species; for estimates from field studies compared with experimental studies; and for within-flock estimates compared with between-flock estimates. Simulation models should reflect this observed variation so as to produce more reliable outputs and support decision-making. How to incorporate this information into simulation models remains a challenge.

Avian influenza

Previous introductions of highly pathogenic avian influenza virus (HPAIV) to the EU were most likely via migratory wild birds. A mathematical model has been developed which indicated that virus amplification and spread may take place when wild bird populations of sufficient size within EU become infected. Low pathogenic avian influenza virus (LPAIV) may reach similar maximum prevalence levels in wild bird populations to HPAIV but the risk of LPAIV infection of a poultry holding was estimated to be lower than that of HPAIV. Only few non-wild bird pathways were identified having a non-negligible risk of AI introduction. The transmission rate between animals within a flock is assessed to be higher for HPAIV than LPAIV. In very few cases, it could be proven that HPAI outbreaks were caused by intrinsic mutation of LPAIV to HPAIV but current knowledge does not allow a prediction as to if, and when this could occur. In gallinaceous poultry, passive surveillance through notification of suspicious clinical signs/mortality was identified as the most effective method for early detection of HPAI outbreaks. For effective surveillance in anseriform poultry, passive surveillance through notification of suspicious clinical signs/mortality needs to be accompanied by serological surveillance and/or a virological surveillance programme of birds found dead (bucket sampling). Serosurveillance is unfit for early warning of LPAI outbreaks at the individual holding level but could be effective in tracing clusters of LPAIV-infected holdings. In wild birds, passive surveillance is an appropriate method for HPAIV surveillance if the HPAIV infections are associated with mortality whereas active wild bird surveillance has a very low efficiency for detecting HPAIV. Experts estimated and emphasised the effect of implementing specific biosecurity measures on reducing the probability of AIV entering into a poultry holding. Human diligence is pivotal to select, implement and maintain specific, effective biosecurity measures.

Assessment of listing and categorisation of animal diseases within the framework of the Animal Health Law (Regulation (EU) No 2016/429): low pathogenic avian influenza

Low pathogenic avian influenza (LPAI) has been assessed according to the criteria of the Animal Health Law (AHL), in particular criteria of Article 7 on disease profile and impacts, Article 5 on the eligibility of LPAI to be listed, Article 9 for the categorisation of LPAI according to disease prevention and control rules as in Annex IV and Article 8 on the list of animal species related to LPAI. The assessment has been performed following a methodology composed of information collection and compilation, expert judgement on each criterion at individual and, if no consensus was reached before, also at collective levels. The output is composed of the categorical answer, and for the questions where no consensus was reached, the different supporting views are reported. Details on the methodology used for this assessment are explained in a separate opinion. According to the assessment performed, LPAI can be considered eligible to be listed for Union intervention as laid down in Article 5(3) of the AHL. The disease would comply with the criteria as in Sections 3 and 5 of Annex IV of the AHL, for the application of the disease prevention and control rules referred to in points (c) and (e) of Article 9(1). The animal species to be listed for LPAI according to Article 8(3) criteria are all species of domestic poultry and wild species of mainly Anseriformes and Charadriiformes, as indicated in the present opinion.