The epidemiological associations between poultry farm biosecurity measures and the 2016/18 highly pathogenic avian influenza (HPAI) epidemics were evaluated using a multivariate logistic model. In the model, 11 biosecurity measures were used as independent variables in the model: a security fence to keep wild birds out of the farm, a security gate on the farm, a farm signboard, number of footbaths for disinfecting footwear, number of anterooms, U-shaped disinfection farm gate, a tunnel-shaped disinfection farm gate, a high-pressure disinfectant fogging farm gate, disinfectant booth for farm workers and visitors, high-pressure disinfectant sprayer in the farm, and personnel disinfectant sprayer in the farm. Two hundred and eighty-eight poultry farms (144 HPAI-confirmed and 144 non-confirmed) were used as the dependent variable. The numbers of footbaths and anterooms were converted to a categorical measurement format using a general additive model. The likelihood of an HPAI outbreak in a poultry farm with a fence to prevent contact between wild birds and domestic fowl was less than that of farms without a fence (OR: 0.54, P value: 0.01). The Akaike information criterion score of the multivariate model (370.91) was less than that of the univariate logistic model for each biosecurity measure. From an HPAI control perspective, it is recommended for poultry farmers to construct a wild bird-proof fence to decrease the HPAI outbreak risk.

The mallard and spot-billed duck are representative migratory bird species wintering in the Republic of Korea. They can be a highly pathogenic avian influenza (HPAI) virus carrier during their wintering movement. From September 2014 to June 2015, 162 poultry farms were confirmed to have a HPAI infection. The current study estimated the home range of the mallard and spot-billed duck during the 2014/15 HPAI epidemics to explore the relationship between the wintering site of the migratory birds and the geographical locations of HPAI-infected farms. A Brownian bridge movement model was applied to estimate the home ranges of 13 mallards and three spot-billed ducks. As a result, 22 HPAI-infected poultry farms were located geographically in the 99% cumulative probability contour of the home range of the mallard, but no HPAI-infected poultry farm was found in spot-billed duck’s home range. In the case of one spot-billed duck, however, it has two wintering sites: Chungcheongnam-do and Jeollanam-do. Considering that migratory birds can be a major driven factor in HPAI virus transmission from wild birds to poultry farms, it is recommended for poultry farms located within the home range of migratory birds to increase their biosecurity level during wintering season of migratory birds.

The current study explored the epidemiological associations between the 2016/18 highly pathogenic avian influenza (HPAI) epidemics and spatial factors, including the distance from a poultry farm to the closest groundwater source, migratory bird habitat, eco-natural area, and poultry farm altitude. We included 14 spatial factors as independent variables. The variables were used in the original continuous measurement format. In total, 288 poultry farms (144 HPAI-confirmed and 144 non-confirmed) were used as the dependent variable. In addition, the variables’ continuous measurement was converted to a categorical measurement format by using a general additive model. For risk factor analysis based on the continuous measurements of spatial factors, the non-graded eco-natural area distance (odds ratio [OR]: 1.00) and the grade one eco-natural area distance (OR: 0.99) were statistically significant independent variables. However, in the risk factor analysis based on the categorical measurement format of the spatial factors, the non-graded eco-natural area distance (OR: 0.08) and poultry farm altitude (OR: 0.44) were statistically significant independent variables in both a univariate and multiple logistic regression model. In other words, when a poultry farm was located far from the non-graded eco-natural area or in a highland area, the likelihood of an HPAI epidemic would decrease. From an HPAI control perspective, it is recommended that the government apply increased levels of biosecurity measures, such as bird-nets, fences, intensive disinfection of equipment, and regular bird health monitoring, for poultry farms located near non-graded eco-natural areas or in a lowland area.

The current study explored the movement characteristics of 14 migratory bird species that wintered in the Republic of Korea between 2014 and 2020. The migratory bird movement information was obtained via a global positioning system operated by the Korean government. The velocity of movement, number of clusters, and size of clusters of the migratory bird species during their movement from their departing country to the Republic of Korea were estimated by applying a method based on density-based spatial clustering of applications with noise. The average movement velocity of pintails (Anas acuta) that departed from China or Russia was 32.77 km/h, the highest velocity among those measured for the 14 migratory bird species. The average number of clusters for cinereous vultures (Aegypius monachus) was 43.00, which was the largest cluster number observed. However, herring gulls (Larus argentatus) had the largest cluster area with an average cluster radius of 27.43 km while wintering in the Republic of Korea. The findings of the current study could be useful in increasing the effectiveness of the Korean national highly pathogenic avian influenza (HPAI) surveillance program. The human and material resources of the HPAI surveillance could be allocated after considering the results of this study, revealing the movement characteristics of wintering migratory birds in Korea. The HPAI surveillance program should include fecal or swab sampling to detect the HPAI virus in both pintail and bean goose (Anser faballis) wintering sites. Sampling of those sites should have a higher priority than that for other migratory bird wintering sites since pintail and bean goose move faster and form larger clusters.

Since the first HPAI epidemics in 2003, there has been little epidemiological research on the association between HPAI epidemics and vehicle movements around poultry farms. This study examined the relationship between vehicle movements around poultry farms and the 2014/15 HPAI epidemics in the Republic of Korea using two methods: a boosted regression trees (BRT) model and logistic regression of a generalized linear model (GLM). The BRT model considers the non-linearity association between the frequency of vehicle movements around poultry farms and the HPAI outbreak status per province using a machine learning technique. In contrast, a GLM assesses the relationship based on the traditional frequentist method. Among the three types of vehicle movements (outbound, inbound, and within), only the outbound was found to be a risk factor of the 2014/15 HPAI epidemics according to both the BRT model and multivariate logistic regression of GLM. In the BRT model results, the median relative contribution of the log-transformed outbound variable was 53.68 (range: 39.99 – 67.58) in the 2014 epidemics and 49.79 (range: 33.90 – 56.38) in the 2015 epidemics. In the GLM results, the odds ratio of the log-transformed outbound variable was 1.22 for the 2014 HPAI epidemics (p < 0.001) and 2.48 for the 2015 HPAI epidemics (p < 0.001), respectively. The results indicated that intensive disinfection measures on outbound movement were needed to reduce the risk of HPAI spread. The current BRT models are suitable for risk analysis because the median area under the receiver operating characteristic curve was 0.83 (range: 0.74 – 0.91) and 0.85 (range: 0.73 – 0.87) for the 2014 and 2015 epidemics models, respectively. The Akaike information criterion scores for the multivariate logistic regression of GLM were 150.27 and 78.21 for the 2014 and 2015 epidemics models, respectively. These scores were relatively lower than those from the univariate logistic regression of GLM.

The goal of the current study was to estimate the contribution of poultry farm vehicle movement frequency to the 2014 highly pathogenic avian influenza (HPAI) epidemic using both global and local regression models. On one hand, the global model did not consider the hypothesis that a relationship between predictors and the outcome variable might vary across the country (spatially homogeneous), while on the other hand, the local model considered that there was spatial heterogeneity within the country. The HPAI outbreak status in each province was used as a dependent variable and the number of poultry farm vehicle movements within each province (within variable), the number of poultry farm vehicle movement from one province to another province (outbound variable), the number of poultry farm vehicle movements from other provinces to one province (inbound variable), and the number of poultry farms in each province were included in the model as independent variables. The results of a global model were as follows: estimated coefficient of the log-transformed within variable was 0.73, that of the log-transformed outbound variable was 2.04, that of the log-transformed inbound variable was 0.74, and that of the number of poultry farms was 1.08. Only the number of poultry farms was a statistically significant variable (p-value < 0.001). The AIC score of the global model was 1397.5. The results of the local model were as follows: estimated median coefficient of the log-transformed within variable was 0.75, that of the log-transformed outbound variable was 2.54, that of the log-transformed inbound variable was 0.60, and that of the number of poultry farms was 0.07. The local model’s AIC score was 1382.2. The results of our study indicate that a local model would provide a better understanding of the relationship between HPAI outbreak status and poultry farm vehicle movements than that provided by a global model.

The goal of the current study was to explore the relationship between vehicle movement frequency and a disease outbreak by using the example of the highly pathogenic avian influenza (HPAI) outbreak in 2014 in the Republic of Korea. To explore the relationship between the HPAI outbreak status of Korean provinces and vehicle movements, both an ordinary least square model (OLS) and a maximum entropy model (MaxEnt) were built. The HPAI outbreak status of each province was used as a dependent variable. The number of poultry farm vehicle movements within the province (within variable), the number of poultry farm vehicle movements from one province to another province (outbound variable), the number of poultry farm vehicle movements from other provinces to one province (inbound variable), and the number of poultry farms in each province were included in the models as independent variables. Results of the OLS model were as follows: the estimated coefficient of the log-transformed within variable was -0.30, that of the log-transformed outbound variable was 0.71, that of the log-transformed inbound variable was -0.30, and that of the number of poultry farms was 0.07; however, only the number of poultry farms per province was statistically significant. Results of the MaxEnt model were as follows: the median relative contribution of the log-transformed outbound variable was 52.0 (range: 12.2–83.9), that of the log-transformed inbound variable was 34.4 (range: 8.8–83.4), that of the log-transformed within variable was 3.7 (range: 1.8–7.3), and that of the number of poultry farms per province was 0.7 (range: 0.0–11.7). The area under the receiver operating characteristics curve was 0.683. The results of current study should be helpful for planning a national HPAI surveillance program to locate surveillance resources with the consideration of risk level of provinces.

Epizootic HPAIV, H5N6, and H5N8 infections produced severe loss in poultry and wild birds in the Republic of Korea from 2016 to 2017. But pathological lesions and antigen distribution of the novel HPAIV H5N6 clade 2.3.4.4 in natural cases have been rarely reported. Herein, we describe the pathological lesions and antigen localization in chickens (layer and Korean native), ducks, and Japanese quail naturally infected by HPAIV H5N6. Grossly, severe reddening, swelling, and some necrotic foci, which were similar to septicemia or viremia, were observed in skin and many visceral organs including trachea, lung, liver, spleen, and pancreas. Histopathologically, pulmonary congestion and edema, as well as necrotizing hepatitis, splenitis, pancreatitis, myocarditis, and encephalitis were observed. Immunohistochemically, numerous HPAIV antigens were detected in necrotic parenchymal cells and in blood vessels of the respiratory, lymphoid, digestive, urinary, nervous, and cardiovascular systems. The results indicate that HPAIV H5N6 spread to the entire body via blood and caused severe damage throughout the entire body. The HPAI H5N6 clade 2.3.4.4 virus was isolated from samples of all four cases.

In this paper, a mathematical model of regionalization based on graph theory to investigate the patterns induced by movements of livestock vehicles in cities under outbreaks of highly pathogenic avian influenza (HPAI) is proposed. We then compare the results of simulation from the regionalization model to actual HPAI outbreaks in 2016/2017 to evaluate the validity of the model. Specifically, we (1) configured a complex network structure with analytic tools and properties in graph theory to abstract the paths among farms and livestock facilities; (2) employed statistical methods to estimate the possibility of propagation between two clusters; (3) applied the developed method to an actual HPAI outbreak in Korea in 2016 and conducted a simulation to determine if the proposed modeling for regionalization is an effective prediction measure. The clustered regions proposed by the simulation correctly reflected the regional clustering of actual cases, while simultaneously contain the cities exposed to potential damage when separated. Based on these findings, we conclude that our proposed regionalization model is suitable for making policy judgments to establish a preemptive biosecurity system.

An understanding of the geographic distribution of highly pathogenic avian influenza (HPAI) is essential to assessing and managing the risk of introduction of HPAI virus (HPAIV). However, to date, local spatial clustering patterns of HPAI outbreaks in Korea has not been explicitly investigated. We compiled HPAI outbreak data (n=622 cases) from December 2003 to February 2016. Each reported case was geocoded and linked to a digital map of Korea according to its onset location using the geographic information system (GIS). Kernel density estimation was used to explore global patterns of the HPAI outbreaks. We also applied the Getis-Ord G local spatial statistic to identify significant hot spots of high and low abundance by calculating Z-scores. Hot spot analysis revealed that HPAI cases are likely to be distinct clusters of HPAI outbreaks, with the highest risk being in the southwest of the country, specifically in Jeonnam and Jeonbuk provinces, where there are high density poultry populations. More than 16 Si-Gun-Gu (administrative province unit with bandwidth of 30 km) were involved in these high risk areas, indicating that there is likely to be a spatial heterogeneity of HPAI outbreaks within the country. Because of the existence of apparent hot spots, particularly in western regions, along with the increased number of migratory birds in these areas, Korea is at high risk of HPAIV introduction. Taking this challenge into consideration, preemptive and effective targeted surveillance programs for wild birds and poultry farms are highly recommended. Future research should look at the risk factors related to the socio-economic, human and natural environments and the poultry production systems to explain the spatial heterogeneity of HPAI outbreaks.

During the past dozens of years, animal species indigenous to Korea has been emerged as a symbol of healthy and well-being lifestyle. Developing new cross hybrid and making brand in Korean native chickens serve as an example of pursuing a well-being life. However, lack of systematic management. intervention from the small scaled middlemen during the multi-stages of marketing, poor hygiene at moorings and live bird market, and possibility of contacting to wild birds have been pointed out as risk factors to the outbreaks of highly pathogenic avian influenza ([1PM), especially for small back yard flocks. This study describes the schema of husbandry and marketing on Korean native chickens, and their putative associations with the outbreaks of RPM during the last two big epidemics occurred in the Republic of Korea, which were in the year of 2008 and 2010/2011.

Korea had experienced second epidemic highly pathogenic avian influenza (HPAI), and there were the seven affected farms, including two breeder duck farms, from 22nd November 2006 to 6th March 2007. Here, we reported the clinico-pathological characteristics of domestic breeder ducks farms naturally infected with HPAI virus (H5N1). Clinically, the most ducks showed various signs from depression, decreased egg production and feed consumption to even, death. The most commonly gross changes were hepatomegaly, splenomegaly, petechial and ecchymotic hemorrhage on the liver surface, a white stripe on the cardiac muscle, and severely hemorrhagic and deformed eggs. The most significant histopathological changes were necrosis of various cells such as neuron, lymphocytes, cardiac myocytes, hepatocytes, blood vessels and pancreatic acinar epithelium. The viral antigen was mainly detected in the endothelium of blood vessels of various organs and tissues, peripheral nerves and neuronal cells. Based on the above results, we identified that HPAI H5N1 induced systemic infection in the adult breeder ducks.

농가를 방문하는 가금관련업체의 관계자 및 차량은 HPAI 질병 확산의 매개체가 된다. 농가들의 가금관련업체 이용 정보를 이용하면 농가간의 연결을 확인할 수 있고 HPAI 확산 가중 네트워크를 구성할 수 있다. 네트워크 분석중 중심성 측정은 질병에 취약하거나 타 농가에 영향력이 큰 역할을 하는 농가를 분석하는 방법으로 HPAI 초기 확산을 통제하는 방법으로 이용된다. 단, HPAI 바이러스는 네트워크의 연결선 가중치에 따라서 확산 경로가 달라질 수 있다.