Several Avian paramyxoviruses 1 (synonymous with Newcastle disease virus or NDV, used hereafter) classification systems have been proposed for strain identification and differentiation. These systems pioneered classification efforts; however, they were based on different approaches and lacked objective criteria for the differentiation of isolates. These differences have created discrepancies among systems, rendering discussions and comparisons across studies difficult. Although a system that used objective classification criteria was proposed by Diel and co-workers in 2012, the ample worldwide circulation and constant evolution of NDV, and utilization of only some of the criteria, led to identical naming and/or incorrect assigning of new sub/genotypes. To address these issues, an international consortium of experts was convened to undertake in-depth analyses of NDV genetic diversity. This consortium generated curated, up-to-date, complete fusion gene class I and class II datasets of all known NDV for public use, performed comprehensive phylogenetic neighbor-Joining, maximum-likelihood, Bayesian and nucleotide distance analyses, and compared these inference methods. An updated NDV classification and nomenclature system that incorporates phylogenetic topology, genetic distances, branch support, and epidemiological independence was developed. This new consensus system maintains two NDV classes and existing genotypes, identifies three new class II genotypes, and reduces the number of sub-genotypes. In order to track the ancestry of viruses, a dichotomous naming system for designating sub-genotypes was introduced. In addition, a pilot dataset and sub-trees rooting guidelines for rapid preliminary genotype identification of new isolates are provided. Guidelines for sequence dataset curation and phylogenetic inference, and a detailed comparison between the updated and previous systems are included. To increase the speed of phylogenetic inference and ensure consistency between laboratories, detailed guidelines for the use of a supercomputer are also provided. The proposed unified classification system will facilitate future studies of NDV evolution and epidemiology, and comparison of results obtained across the world.

Egypt is a hotspot for avian influenza virus (AIV) due to the endemicity of H5N1 and H9N2 viruses. AIVs were isolated from 329 samples collected in 2016–2018; 48% were H9N2, 37.1% were H5N8, 7.6% were H5N1, and 7.3% were co-infections with 2 of the 3 subtypes. The 32 hemagglutinin (HA) sequences of the H5N1 viruses formed a well-defined lineage within clade 2.2.1.2. The 10 HA sequences of the H5N8 viruses belonged to a subclade within 2.3.4.4. The 11 HA of H9N2 isolates showed high sequence homology with other Egyptian G1-like H9N2 viruses. The prevalence of H5N8 viruses in ducks (2.4%) was higher than in chickens (0.94%). Genetic reassortment was detected in H9N2 viruses. Antigenic analysis showed that H9N2 viruses are homogenous, antigenic drift was detected among H5N1 viruses. AI H5N8 showed higher replication rate followed by H9N2 and H5N1, respectively. H5N8 was more common in Southern Egypt, H9N2 in the Nile Delta, and H5N1 in both areas. Ducks and chickens played a significant role in transmission of H5N1 viruses. The endemicity and co-circulation of H5N1, H5N8, and H9N2 AIV coupled with the lack of a clear control strategy continues to provide avenues for further virus evolution in Egypt.

Background: Avian avulavirus (commonly known as avian paramyxovirus-1 or APMV-1) can cause disease of varying severity in both domestic and wild birds. Understanding how viruses move among hosts and geography would be useful for informing prevention and control efforts. A Bayesian statistical framework was employed to estimate the evolutionary history of 1602 complete fusion gene APMV-1 sequences collected from 1970 to 2016 in order to infer viral transmission between avian host orders and diffusion among geographic regions. Ancestral states were estimated with a non-reversible continuous-time Markov chain model, allowing transition rates between discrete states to be calculated. The evolutionary analyses were stratified by APMV-1 classes I (n = 198) and II (n = 1404), and only those sequences collected between 2006 and 2016 were allowed to contribute host and location information to the viral migration networks. Results: While the current data was unable to assess impact of host domestication status on APMV-1 diffusion, these analyses supported the sharing of APMV-1 among divergent host taxa. The highest supported transition rate for both classes existed from domestic chickens to Anseriformes (class I:6.18 transitions/year, 95% highest posterior density (HPD) 0.31-20.02, Bayes factor (BF) = 367.2; class II:2.88 transitions/year, 95%HPD 1.9-4.06, BF = 34,582.9). Further, among class II viruses, domestic chickens also acted as a source for Columbiformes (BF = 34,582.9), other Galliformes (BF = 34,582.9), and Psittaciformes (BF = 34,582.9). Columbiformes was also a highly supported source to Anseriformes (BF = 322.0) and domestic chickens (BF = 402.6). Additionally, our results provide support for the diffusion of viruses among continents and regions, but no interhemispheric viral exchange between 2006 and 2016. Among class II viruses, the highest transition rates were estimated from South Asia to the Middle East (1.21 transitions/year; 95%HPD 0.36-2.45; BF = 67,107. 8), from Europe to East Asia (1.17 transitions/year; 95%HPD 0.12-2.61; BF = 436.2) and from Europe to Africa (1.06 transitions/year, 95%HPD 0.07-2.51; BF = 169.3). Conclusions: While migration appears to occur infrequently, geographic movement may be important in determining viral diversification and population structure. In contrast, inter-order transmission of APMV-1 may occur readily, but most events are transient with few lineages persisting in novel hosts.

Eurasia highly pathogenic avian influenza virus (HPAIV) H5 clade 2.3.4.4 emerged in North America at the end of 2014 and caused outbreaks affecting >50 million poultry in the United States before eradication in June 2015. We investigated the underlying ecologic and epidemiologic processes associated with this viral spread by performing a comparative genomic study using 268 full-length genome sequences and data from outbreak investigations. Reassortant HPAIV H5N2 circulated in wild birds along the Pacific flyway before several spillover events transmitting the virus to poultry farms. Our analysis suggests that >3 separate introductions of HPAIV H5N2 into Midwest states occurred during March–June 2015; transmission to Midwest poultry farms from Pacific wild birds occurred ≈1.7–2.4 months before detection. Once established in poultry, the virus rapidly spread between turkey and chicken farms in neighboring states. Enhanced biosecurity is required to prevent the introduction and dissemination of HPAIV across the poultry industry.