Ecological drivers for poultry farms predisposed to highly pathogenic avian influenza virus infection during the initial phase of the six outbreaks between 2010-2021: a nationwide study in South Korea

Background

Highly pathogenic avian influenza (HPAI) has caused substantial economic losses worldwide. An understanding of the environmental drivers that contribute to spillover transmission from wild birds to poultry farms is important for predicting areas at risk of introduction and developing risk-based surveillance strategies. We conducted an epidemiological study using data from six HPAI outbreak events in South Korea.

Materials and methods

An aggregate-level study design was implemented using third-level administrative units in South Korea. Only regions with high natural reservoir suitability were included. The incidence of HPAI at chicken and duck farms during the initial phase (30 and 45 days after the first case) of each outbreak event was used as the outcome variable, assuming that cross-species transmission from wild birds was the dominant exposure leading to infection. Candidate environmental drivers were meteorological factors, including temperature, precipitation, humidity, and altitude, as well as the proportion of protected area, farm density, deforestation level, and predator species richness. Logistic regression models were implemented; conditional autoregression models were used in cases of spatial autocorrelation of residuals.

Results

Lower temperature, higher farm density, and lower predator species richness were significantly associated with a higher risk of HPAI infection on chicken farms. Lower temperature, higher proportion of protected area, and lower predator species richness were significantly associated with a higher risk of HPAI infection on duck farms.

Conclusion

The predicted dominant transmission routes on chicken and duck farms were horizontal and spillover, respectively. These results reveal a potential protective effect of predator species richness against HPAI outbreaks. Further studies are required to confirm a causal relationship.

Phylodynamic approaches to studying avian influenza virus

Avian influenza viruses can cause severe disease in domestic and wild birds and are a pandemic threat. Phylodynamics is the study of how epidemiological, evolutionary, and immunological processes can interact to shape viral phylogenies. This review summarizes how phylodynamic methods have and could contribute to the study of avian influenza viruses. Specifically, we assess how phylodynamics can be used to examine viral spread within and between wild or domestic bird populations at various geographical scales, identify factors associated with virus dispersal, and determine the order and timing of virus lineage movement between geographic regions or poultry production systems. We discuss factors that can complicate the interpretation of phylodynamic results and identify how future methodological developments could contribute to improved control of the virus.

Genome sequence of a Sobemovirus from the feces of Northern Pintail (Anas acuta)

Northern pintail (Anas acuta) is a migratory waterfowl that can transmit various viruses. The genome sequence of a Sobemovirus was determined using metagenomic sequencing from the feces of northern pintail (Anas acuta) in Xinjiang, northwest China. The virus possesses a linear RNA molecule of 4177 bp and is most closely related to isolates SoMV-WA (GenBank accession no. HM163159.1) and ATCC PV-109 (GenBank accession no. GQ845002.2), with a nucleotide identity of 86.7%. The virus encodes four open reading frames (ORF) coding for four proteins, and phylogenetic analysis of capsid protein and RNA-dependent RNA polymerase (RdRp) showed that the strain was clustered into the species Sowbane Mosaic Virus (SoMV). The amino acid sequence identity of capsid protein was 89.6-90.9% to other isolates of SoMV, but 17.6-31.4% similar to other strains in the genus Sobemovirus, indicating a strain of Sowbane Mosaic Virus. This is the first report of SoMV in the feces of wild birds and in China, and it suggested that northern pintail likely plays an alternative role in the transmission of SoMV.

North American wintering mallards infected with highly pathogenic avian influenza show few signs of altered local or migratory movements

Avian influenza viruses pose a threat to wildlife and livestock health. The emergence of highly pathogenic avian influenza (HPAI) in wild birds and poultry in North America in late 2021 was the first such outbreak since 2015 and the largest outbreak in North America to date. Despite its prominence and economic impacts, we know relatively little about how HPAI spreads in wild bird populations. In January 2022, we captured 43 mallards (Anas platyrhynchos) in Tennessee, USA, 11 of which were actively infected with HPAI. These were the first confirmed detections of HPAI H5N1 clade 2.3.4.4b in the Mississippi Flyway. We compared movement patterns of infected and uninfected birds and found no clear differences; infected birds moved just as much during winter, migrated slightly earlier, and migrated similar distances as uninfected birds. Infected mallards also contacted and shared space with uninfected birds while on their wintering grounds, suggesting ongoing transmission of the virus. We found no differences in body condition or survival rates between infected and uninfected birds. Together, these results show that HPAI H5N1 clade 2.3.4.4b infection was unrelated to body condition or movement behavior in mallards infected at this location during winter; if these results are confirmed in other seasons and as HPAI H5N1 continues to evolve, they suggest that these birds could contribute to the maintenance and dispersal of HPAI in North America. Further research on more species across larger geographic areas and multiple seasons would help clarify potential impacts of HPAI on waterfowl and how this emerging disease spreads at continental scales, across species, and potentially between wildlife and domestic animals.

Habitat loss exacerbates pathogen spread: An Agent-based model of avian influenza infection in migratory waterfowl

Habitat availability determines the distribution of migratory waterfowl along their flyway, which further influences the transmission and spatial spread of avian influenza viruses (AIVs). The extensive habitat loss in the East Asian-Australasian Flyway (EAAF) may have potentially altered the virus spread and transmission, but those consequences are rarely studied. We constructed 6 fall migration networks that differed in their level of habitat loss, wherein an increase in habitat loss resulted in smaller networks with fewer sites and links. We integrated an agent-based model and a susceptible-infected-recovered model to simulate waterfowl migration and AIV transmission. We found that extensive habitat loss in the EAAF can 1) relocate the outbreaks northwards, responding to the distribution changes of wintering waterfowl geese, 2) increase the outbreak risk in remaining sites due to larger goose congregations, and 3) facilitate AIV transmission in the migratory population. In addition, our modeling output was in line with the predictions from the concept of "migratory escape", i.e., the migration allows the geese to "escape" from the location where infection risk is high, affecting the pattern of infection prevalence in the waterfowl population. Our modeling shed light on the potential consequences of habitat loss in spreading and transmitting AIV at the flyway scale and suggested the driving mechanisms behind these effects, indicating the importance of conservation in changing spatial and temporal patterns of AIV outbreaks.

Interpolation of Point Prevalence Rate of the Highly Pathogenic Avian Influenza Subtype H5N8 Second Phase Epidemic in South Korea

Humans and animals are both susceptible to highly pathogenic avian influenza (HPAI) viruses. In the future, HPAI has the potential to be a source of zoonoses and pandemic disease drivers. It is necessary to identify areas of high risk that are more vulnerable to HPAI infections. In this study, we applied unbiased predictions based on known information to find points of localities with a high probability of point prevalence rate. To carry out such predictions, we utilized the inverse distance weighting (IDW) and kriging method, with the help of the R statistical computing program. The provinces of Jeollanam-do, Gyeonggi-do, Chungcheongbuk-do and Ulsan have high anticipated risk. This research might aid in the management of avian influenza threats associated with various potential risks.

Spatial Variation in Risk for Highly Pathogenic Avian Influenza Subtype H5N6 Viral Infections in South Korea: Poultry Population-Based Case–Control Study

Given the substantial economic damage caused by the continual circulation of highly pathogenic avian influenza (HPAI) outbreaks since 2003, identifying high-risk locations associated with HPAI infections is essential. In this study, using affected and unaffected poultry farms’ locations during an HPAI H5N6 epidemic in South Korea, we identified places where clusters of HPAI cases were found. Hotspots were defined as regions having clusters of HPAI cases. With the help of the statistical computer program R, a kernel density estimate and a spatial scan statistic were employed for this purpose. A kernel density estimate and detection of significant clusters through a spatial scan statistic both showed that districts in the Chungcheongbuk-do, Jeollabuk-do, and Jeollanam-do provinces are more vulnerable to HPAI outbreaks. Prior to the migration season, high-risk districts should implement particular biosecurity measures. High biosecurity measures, as well as improving the cleanliness of the poultry environment, would undoubtedly aid in the prevention of HPAIV transmission to poultry farms in these high-risk regions of South Korea.

Avian Influenza in Wild Birds and Poultry: Dissemination Pathways, Monitoring Methods, and Virus Ecology

Avian influenza is one of the largest known threats to domestic poultry. Influenza outbreaks on poultry farms typically lead to the complete slaughter of the entire domestic bird population, causing severe economic losses worldwide. Moreover, there are highly pathogenic avian influenza (HPAI) strains that are able to infect the swine or human population in addition to their primary avian host and, as such, have the potential of being a global zoonotic and pandemic threat. Migratory birds, especially waterfowl, are a natural reservoir of the avian influenza virus; they carry and exchange different virus strains along their migration routes, leading to antigenic drift and antigenic shift, which results in the emergence of novel HPAI viruses. This requires monitoring over time and in different locations to allow for the upkeep of relevant knowledge on avian influenza virus evolution and the prevention of novel epizootic and epidemic outbreaks. In this review, we assess the role of migratory birds in the spread and introduction of influenza strains on a global level, based on recent data. Our analysis sheds light on the details of viral dissemination linked to avian migration, the viral exchange between migratory waterfowl and domestic poultry, virus ecology in general, and viral evolution as a process tightly linked to bird migration. We also provide insight into methods used to detect and quantify avian influenza in the wild. This review may be beneficial for the influenza research community and may pave the way to novel strategies of avian influenza and HPAI zoonosis outbreak monitoring and prevention.

Continental-scale dynamics of avian influenza in U.S. waterfowl are driven by demography, migration, and temperature

Emerging diseases of wildlife origin are increasingly spilling over into humans and domestic animals. Surveillance and risk assessments for transmission between these populations are informed by a mechanistic understanding of the pathogens in wildlife reservoirs. For avian influenza viruses (AIV), much observational and experimental work in wildlife has been conducted at local scales, yet fully understanding their spread and distribution requires assessing the mechanisms acting at both local, (e.g., intrinsic epidemic dynamics), and continental scales, (e.g., long-distance migration). Here, we combined a large, continental-scale data set on low pathogenic, Type A AIV in the United States with a novel network-based application of bird banding/recovery data to investigate the migration-based drivers of AIV and their relative importance compared to well-characterized local drivers (e.g., demography, environmental persistence). We compared among regression models reflecting hypothesized ecological processes and evaluated their ability to predict AIV in space and time using within and out-of-sample validation. We found that predictors of AIV were associated with multiple mechanisms at local and continental scales. Hypotheses characterizing local epidemic dynamics were strongly supported, with age, the age-specific aggregation of migratory birds in an area and temperature being the best predictors of infection. Hypotheses defining larger, network-based features of the migration processes, such as clustering or between-cluster mixing explained less variation but were also supported. Therefore, our results support a role for local processes in driving the continental distribution of AIV.

Landscape effects and spatial patterns of avian influenza virus in Danish wild birds, 2006-2020

Avian influenza (AI) is a contagious disease of birds with zoonotic potential. AI virus (AIV) can infect most bird species, but clinical signs and mortality vary. Assessing the distribution and factors affecting AI presence can direct targeted surveillance to areas at risk of disease outbreaks, or help identify disease hotspots or areas with inadequate surveillance. Using virus surveillance data from passive and active AIV wild bird surveillance, 2006−2020, we investigated the association between the presence of AIV and a range of landscape factors and game bird release. Furthermore, we assessed potential bias in the passive AIV surveillance data submitted by the public, via factors related to public accessibility. Lastly, we tested the AIV data for possible hot‐ and cold spots within Denmark. The passive surveillance data was biased regarding accessibility to areas (distance to roads, cities and coast) compared to random locations within Denmark. For both the passive and active AIV surveillance data, we found significant (p < .01) associations with variables related to coast, wetlands and cities, but not game bird release. We used these variables to predict the risk of AIV presence throughout Denmark, and found high‐risk areas concentrated along the coast and fjords. For both passive and active surveillance data, low‐risk clusters were mainly seen in Jutland and northern Zealand, whereas high‐risk clusters were found in Jutland, Zealand, Funen and the southern Isles such as Lolland and Falster. Our results suggest that landscape affects AIV presence, as coastal areas and wetlands attract waterfowl and migrating birds and therefore might increase the potential for AIV transmission. Our findings have enabled us to create risk maps of AIV presence in wild birds and pinpoint high‐risk clusters within Denmark. This will aid targeted surveillance efforts within Denmark and potentially aid in planning the location of future poultry farms.

Surveillance system for avian influenza in wild birds and implications of its improvement with insights into the highly pathogenic avian influenza outbreaks in Japan

Since the re-emergence of a highly pathogenic avian influenza (HPAI) in 2004, outbreaks of the viral subtypes HPAI, H5N1, H5N8, and H5N6 in wild birds, poultry, and zoo birds have occurred in Japan. In 2008, a nation-wide avian influenza (AI) surveillance program was started for the early detection of the HPAI virus (HPAIV) and for the assessment of HPAIV infection among wild birds. In this study, we aimed to conduct an overview of the AI surveillance system of wild birds in Japan, including those in the regions and prefectures, to assess its overall performance and develop insights on its improvement. We analyzed past surveillance data in Japan and conducted questionnaire surveys for the officers in 11 regional branches of the Ministry of Environment and the nature conservation divisions of 47 prefectures to acquire details regarding those AI surveillance. We found that the early detection of HPAIV in wild birds was successfully achieved in only one of the five outbreak seasons during the 2008-2019 period in Japan, and the assessment of HPAIV infection had possibly not been adequate in the national surveillance system. In the winter season, AI surveillance in most prefectures were mainly conducted by means of passive surveillance through reported dead birds and active surveillance through collected waterbird feces. Conversely, less than half of the prefectures conducted bird monitoring, patrolling in migratory bird habitats, and AI antigen testing in rescued birds. In areas surrounding HPAI occurrence sites (<10 km), bird monitoring and patrolling efforts were enhanced. However, AI testing efforts in waterbird feces and rescued birds were decreased. The AI surveillance for endangered bird species and in national wildlife protection areas was conducted by the branches of the Ministry of Environment and by the prefectures. Based on our results, we concluded that for maximum efficiency, legislation which specialized in wildlife pathogens should be necessary to prepare adequate national budget and testing capacity for appropriate surveillance system with periodical assessment for surveillance results and the system.

Population genomics of louping ill virus provide new insights into the evolution of tick-borne flaviviruses

The emergence and spread of tick-borne arboviruses pose an increased challenge to human and animal health. In Europe this is demonstrated by the increasingly wide distribution of tick-borne encephalitis virus (TBEV, Flavivirus, Flaviviridae), which has recently been found in the United Kingdom (UK). However, much less is known about other tick-borne flaviviruses (TBFV), such as the closely related louping ill virus (LIV), an animal pathogen which is endemic to the UK and Ireland, but which has been detected in other parts of Europe including Scandinavia and Russia. The emergence and potential spatial overlap of these viruses necessitates improved understanding of LIV genomic diversity, geographic spread and evolutionary history. We sequenced a virus archive composed of 22 LIV isolates which had been sampled throughout the UK over a period of over 80 years. Combining this dataset with published virus sequences, we detected no sign of recombination and found low diversity and limited evidence for positive selection in the LIV genome. Phylogenetic analysis provided evidence of geographic clustering as well as long-distance movement, including movement events that appear recent. However, despite genomic data and an 80-year time span, we found that the data contained insufficient temporal signal to reliably estimate a molecular clock rate for LIV. Additional analyses revealed that this also applied to TBEV, albeit to a lesser extent, pointing to a general problem with phylogenetic dating for TBFV. The 22 LIV genomes generated during this study provide a more reliable LIV phylogeny, improving our knowledge of the evolution of tick-borne flaviviruses. Our inability to estimate a molecular clock rate for both LIV and TBEV suggests that temporal calibration of tick-borne flavivirus evolution should be interpreted with caution and highlight a unique aspect of these viruses which may be explained by their reliance on tick vectors.

Tick-borne pathogens represent a major emerging threat to public health and in recent years have been expanding into new areas. LIV is a neglected virus endemic to the UK and Ireland (though it has been detected in Scandinavia and Russia) which is closely related to the major human pathogen TBEV, but predominantly causes disease in sheep and grouse. The recent detection of TBEV in the UK, which has also emerged elsewhere in Europe, requires more detailed understanding of the spread and sequence diversity of LIV. This could be important for diagnosis and vaccination, but also to improve our understanding of the evolution and emergence of these tick-borne viruses. Here we describe the sequencing of 22 LIV isolates which have been sampled from several host species across the past century. We have utilised this dataset to investigate the evolutionary pressures that LIV is subjected to and have explored the evolution of LIV using phylogenetic analysis. Crucially we were unable to estimate a reliable molecular clock rate for LIV and found that this problem also extends to a larger phylogeny of TBEV sequences. This work highlights a previously unknown caveat of tick-borne flavivirus evolutionary analysis which may be important for understanding the evolution of these important pathogens.

Waterfowl occurrence and residence time as indicators of H5 and H7 avian influenza in North American Poultry

Avian influenza (AI) affects wild aquatic birds and poses hazards to human health, food security, and wildlife conservation globally. Accordingly, there is a recognized need for new methods and tools to help quantify the dynamic interaction between wild bird hosts and commercial poultry. Using satellite-marked waterfowl, we applied Bayesian joint hierarchical modeling to concurrently model species distributions, residency times, migration timing, and disease occurrence probability under an integrated animal movement and disease distribution modeling framework. Our results indicate that migratory waterfowl are positively related to AI occurrence over North America such that as waterfowl occurrence probability or residence time increase at a given location, so too does the chance of a commercial poultry AI outbreak. Analyses also suggest that AI occurrence probability is greatest during our observed waterfowl northward migration, and less during the southward migration. Methodologically, we found that when modeling disparate facets of disease systems at the wildlife-agriculture interface, it is essential that multiscale spatial patterns be addressed to avoid mistakenly inferring a disease process or disease-environment relationship from a pattern evaluated at the improper spatial scale. The study offers important insights into migratory waterfowl ecology and AI disease dynamics that aid in better preparing for future outbreaks.