Dynamics of inter-farm transmission of highly pathogenic avian influenza H5N6 integrating vehicle movements and phylogenetic information

The role of vehicle movement in swine disease dissemination: Novel method accounting for pathogen stability and vehicle cleaning effectiveness uncertainties

Several propagation routes drive animal disease dissemination, and among these routes, contaminated vehicles traveling between farms have been associated with indirect disease transmission. In this study, we used near-real-time vehicle movement data and vehicle cleaning efficacy to reconstruct the between-farm dissemination of the African swine fever virus (ASFV). We collected one year of Global Positioning System data of 823 vehicles transporting feed, pigs, and people to 6363 swine production farms in two regions in the U.S. Without cleaning, vehicles connected up to 2157 farms in region one and 437 farms in region two. Individually, in region one vehicles transporting feed connected 2151 farms, pigs to farms 2089 farms, pigs to market 1507 farms, undefined vehicles 1760 farm, and personnel three farms. The simulation results indicated that the contact networks were reduced the most for crew transport vehicles with a 66% reduction, followed by vehicles carrying pigs to market and farms, with reductions of 43% and 26%, respectively, when 100% cleaning efficacy was achieved. The results of this study showed that even when vehicle cleaning and disinfection are 100% effective, vehicles are still connected to numerous farms. This emphasizes the importance of better understanding transmission risks posed by vehicles to the swine industry and regulatory agencies.

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.

A systematic review of mechanistic models used to study avian influenza virus transmission and control

The global spread of avian influenza A viruses in domestic birds is causing increasing socioeconomic devastation. Various mechanistic models have been developed to better understand avian influenza transmission and evaluate the effectiveness of control measures in mitigating the socioeconomic losses caused by these viruses. However, the results of models of avian influenza transmission and control have not yet been subject to a comprehensive review. Such a review could help inform policy makers and guide future modeling work. To help fill this gap, we conducted a systematic review of the mechanistic models that have been applied to field outbreaks. Our three objectives were to: (1) describe the type of models and their epidemiological context, (2) list estimates of commonly used parameters of low pathogenicity and highly pathogenic avian influenza transmission, and (3) review the characteristics of avian influenza transmission and the efficacy of control strategies according to the mechanistic models. We reviewed a total of 46 articles. Of these, 26 articles estimated parameters by fitting the model to data, one evaluated the effectiveness of control strategies, and 19 did both. Values of the between-individual reproduction number ranged widely: from 2.18 to 86 for highly pathogenic avian influenza viruses, and from 4.7 to 45.9 for low pathogenicity avian influenza viruses, depending on epidemiological settings, virus subtypes and host species. Other parameters, such as the durations of the latent and infectious periods, were often taken from the literature, limiting the models' potential insights. Concerning control strategies, many models evaluated culling (n = 15), while vaccination received less attention (n = 6). According to the articles reviewed, optimal control strategies varied between virus subtypes and local conditions, and depended on the overall objective of the intervention. For instance, vaccination was optimal when the objective was to limit the overall number of culled flocks. In contrast, pre-emptive culling was preferred for reducing the size and duration of an epidemic. Early implementation consistently improved the overall efficacy of interventions, highlighting the need for effective surveillance and epidemic preparedness.

Three Years of African Swine Fever in South Korea (2019–2021): A Scoping Review of Epidemiological Understanding

African swine fever (ASF) is a highly contagious viral disease in domestic pigs and wild boar that causes tremendous socioeconomic damage in related industries. In 2019, the virus emerged in South Korea, which has since reported 21 outbreaks in domestic pig farms and over 2,600 cases in wild boar. In this review, we synthesize the epidemiological knowledge generated on ASF in South Korea during the first three years of the epidemic (2019–2021). We searched four international and one domestic Korean database to identify scientific articles published since 2019 and describing ASF epidemiology in South Korea. Fourteen articles met our selection criteria and were used to synthesize the origin of ASF in South Korea, the risk factors of disease occurrence, the effectiveness of the surveillance and intervention measures that were implemented, and the viral transmission dynamics. We found that timely intensive surveillance and interventions on domestic pig farms successfully blocked between-farm transmission. However, in wild boar, the ASF virus has spread massively towards the south primarily along the mountain ranges despite ongoing fence erection and intensive depopulation efforts, endangering domestic pig farms across the country. The current devastating epidemic is suspected to be the consequence of an ASF control strategy unaligned to the epidemiological context, the challenging implementation of control measures hindered by topological complexities, and inappropriate biosecurity by field workers. To improve our understanding of ASF epidemiology in South Korea and enhance disease management, future research studies should specify the ecological drivers of disease distribution and spread and devise effective control strategies, particularly in relation to Korean topography, and the latent spread of the virus in wild boar populations. Additionally, research studies should explore the psychosocial factors for ASF management, and develop tools to support evidence-based decision-making for managing ASFV in wild boar.

Integration of Epidemiological and Genomic Data to Investigate H5N1 HPAI Outbreaks in Northern Italy in 2021-2022

Between October 2021 and April 2022, 317 outbreaks caused by highly pathogenic avian influenza (HPAI) H5N1 viruses were notified in poultry farms in the northeastern Italian regions. The complete genomes of 214 strains were used to estimate the genetic network based on the similarity of the viruses. An exponential random graph model (ERGM) was used to assess the effect of 'at-risk contacts', 'same owners', 'in-bound/out-bound risk windows overlap', 'genetic differences', 'geographic distances', 'same species', and 'poultry company' on the probability of observing a link within the genetic network, which can be interpreted as the potential propagation of the epidemic via lateral spread or a common source of infection. The variables 'same poultry company' (Est. = 0.548, C.I. = [0.179; 0.918]) and 'risk windows overlap' (Est. = 0.339, C.I. = [0.309; 0.368]) were associated with a higher probability of link formation, while the 'genetic differences' (Est. = -0.563, C.I. = [-0.640; -0.486]) and 'geographic distances' (Est. = -0.058, C.I. = [-0.078; -0.038]) indicated a reduced probability. The integration of epidemiological data with genomic analyses allows us to monitor the epidemic evolution and helps to explain the dynamics of lateral spreads casting light on the potential diffusion routes. The 2021-2022 epidemic stresses the need to further strengthen the biosecurity measures, and to encourage the reorganization of the poultry production sector to minimize the impact of future epidemics.

Genome-associations of extended-spectrum ß-lactamase producing (ESBL) or AmpC producing E. coli in small and medium pig farms from Khon Kaen province, Thailand

Thailand is undergoing rapid intensification of livestock production where small subsistence farms and medium sized commercial farms coexist. In medium farms, antimicrobials are prescribed by a veterinarian, whereas in small farms antimicrobial use remains largely unsupervised. The impact of these differences as well as other farming practices on the emergence and composition of antimicrobial resistance genes (ARGs) remains largely unknown. We analyzed 363 genomes of extended-spectrum ß-lactamase producing (ESBL) and/or AmpC producing Escherichia coli recovered from humans and pigs at small and medium farms from the Khon Kaen province, Thailand. We tested for genome-wide associations to identify links between ARGs, host, and farm size. Pig isolates from small farms were associated with mcr and qnr genes conferring resistance to colistin and fluoroquinolones, respectively. In contrast, pig isolates from medium farms were associated with ARGs conferring resistance to drugs commonly used on medium farms (i.e., streptomycin). ESBL plasmids from small farms co-carried ARGs conferring resistance to critically important antimicrobials more frequently compared to plasmid from medium farms. Frequent ARG combinations included bla_CTX-M-55 + qnrS1 (29.8% vs 17.5% in small and medium farms, respectively), bla_CTX-M-55 + qnrS1 + mcr-3.19 (5% vs 0%), bla_CTX-M-14 + qnrS1 (9.3% vs 6.2%), and bla_CTX-M-14 + qnrS1 + mcr-1.1 (3.1% vs 0%). The co-location on plasmids of ARGs conferring resistance to critically important antimicrobials as defined by the World Health Organization is concerning, and actions to curb their spread are urgently needed. Legislation on limiting antimicrobial sales and initiatives to better inform farmers and veterinarians on appropriate antimicrobial usage and farm biosecurity could help reduce antimicrobial use on farms.

Hierarchical Clustering on Principal Components Analysis to Detect Clusters of Highly Pathogenic Avian Influenza Subtype H5N6 Epidemic across South Korean Poultry Farms

Several outbreaks of highly pathogenic avian influenza (HPAI) in poultry have already been documented across the world, causing major economic losses. Research on diverse perspectives for future HPAI outbreaks’ prevention is desperately needed. It is critical to determine high-risk areas for HPAI outbreaks in order to develop high-level biosecurity in all such areas. The aim of this study is to identify high-risk areas as hotspots for high rates of birds’ infection and mortality and culling. We used “hierarchical clustering on principal components” (HCPC) to classify infected poultry farms in South Korea based on the point prevalence rate, infections, and deaths in susceptible birds. The linear combination of the original predictors was determined using “principal component analysis (PCA)”. Based on PCA, we applied the hierarchical clustering algorithm, which divided the data into four clusters based on the dissimilarity matrix. These four groups of poultry farms were identified on the basis of five variables. According to the findings based on the HCPC method, poultry farms in “cluster 4” had significantly higher average bird infections with high mortality when compared to other clusters. Similarly, the poultry farms in “cluster 2” had robust average bird culling in place to limit bird infectivity and mortality due to a high number of susceptible birds. The poultry farms belonging to “cluster 3” had a significantly higher average point prevalence rate of HPAI H5N6 cases than the rest of the clusters. Based on this study, it is recommended that poultry farms with a high number of infections and mortality in susceptible birds should implement proper biosecurity management to control HPAI infections while avoiding the culling of a large number of birds.

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.