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.

Since the first detection of African swine fever (ASF)-infected wild boar in October 2019, the ASF virus has been circulating among wild boars in the Republic of Korea. The priority for ASF control is to understand the epidemic situation correctly. The basic reproduction number (R0) can be used to describe the contagious disease epidemic situation since it can assess the contagiousness of infectious agents by presenting the average number of new cases generated by an infected case. The current study estimated R0 for the 2019/20 ASF epidemics in wild boars in the Republic of Korea using the reported number of ASF cases and a serial interval of the ASF virus. The estimated mean R0 was 2.10 (range: 0.06 – 10.24) for the 2019/20 ASF epidemics, 2.94 (range: 0.43 – 9.89) for the 2019 ASF epidemics, and 2.00 (range: 0.06 – 11.10) for the 2020 ASF epidemics. In addition, the estimated mean R0 was 3.82 (range: 1.16 – 8.78) in winter, 1.39 (range: 0.16 – 6.30) in spring, 4.82 (range: 0.26 – 17.08) in summer, and 2.21 (range: 0.51 – 5.86) in fall. Even though the Korean government has applied ASF control measures, including hunting or fencing, the ASF epidemic situation in wild boars has intensified. For ASF control in wild boars, tailor-made hunting, wild boar management, or active searching for carcasses are required to reduce the ASF virus. For ASF prevention in domestic pigs, no contact between wild boars and domestic pigs and a biosecurity plan by veterinarians are needed to decrease the risk of ASF virus transmission from wild boars to domestic pigs.

The estimation of the postmortem interval (PMI, the time that has elapsed since the death) is a critical issue for the biosecurity enforcement officers who implement to the timely establishment of biosecurity zone for preventing susceptible animals from disease transmission given the rapid occurrence of an infectious disease. Increasing attention has been paid to PMI of wild boar (Sus scrofa) carcasses associated with African swine fever epidemics in Korea since October 2019 to explain the geographical transmission of the disease, as well as to provide potential target animals for prevention measures in terms of farm biosecurity. This study is, to the best of our knowledge, the first to describe the decomposition process of wild boars in different microhabitats in the country. In the present comparative study, we obtained field data from the decomposition process of the wild boar and domestic pig carcasses continuously exposed aboveground in natural environment settings allowing animal scavenging. This study compared the pattern of decomposition in 16 wild boar carrion and 10 domestic pig carcasses placed between August and November 2019. Quite differences in decomposition rate measured by total body score and tissue's gross morphology over time were observed between wild boars and domestic pigs. Overall, the wild boar decomposed much more slowly than the domestic pig throughout the end of the experimental period. In addition, color changes to the skin were easily seen in domestic pigs, whereas there is much variation in the wild boar, especially carcasses placed in late autumn. Moreover, some wild boar carcasses did not show any sign of bloating. These results indicate that decomposition rates derived from forensic taphonomic studies on domestic pigs may be not directly applicable to the wild boar, hence there is a need to develop regional decomposition models to be employed in different geographical situations to increase the accuracy of PMI of wild boar carcasses.

Since the 2010 foot and mouth (FMD) epidemic, the Korean government has applied a FMD vaccination to pigs. A FMD vaccine is injected to pigs by intramuscular (IM) route. One of the drawbacks in FMD vaccine IM injection is that it would result in an abnormal meat on the injection spot. An abnormal meat due to FMD vaccine IM injection would cause economic loss to pig farmers. An intradermal (ID) injection would be an alternative method for FMD vaccination. The goal of current study was to compare the antibody formation rate between FMD vaccine IM injection and ID injection. The antibody formation rate was measured by the FMD serotype O vaccination percent inhibition (PI) value. In total 350 pigs (175 for FMD vaccine IM injection and 175 for FMD vaccine ID injection) were included in the study. In results, the PI values of FMD vaccine IM injection group were significantly higher than it of FMD vaccine ID injection group. However, the proportions of pigs with PI value was higher than 50, which is a legislative requirement for marketing pigs, for both FMD vaccine IM and ID injection groups at week 20 or 23 were not significantly different. The current results indicated that a FMD vaccine ID injection could be an alternative method of IM injection.

Since the first detection of the African swine fever (ASF) virus in the Republic of Korea in 2019, the Korean government has applied interventions, including fencing, increasing the biosecurity level at domestic pig farms, and the capture-and-removal of wild boars. In particular, wild boars are an important risk factor for ASF control because they can spread disease among susceptible animals, such as wild boars or domestic pigs. A capture-and-removal method aims to reduce the likelihood of ASF transmission from wild boars to domestic boars or among wild boars by decreasing the number of susceptible wild boars. This study estimated the required number of wild boars captured and removed for ASF control using population viability analysis. Population factors, such as a life span, sex ratio, or an inbreeding depression with different capture-and-removal proportions of wild boars, were included in the analysis. Ten scenarios with different capture-and-removal proportions of wild boars and different periods of culling were considered. According to the results, a method in which 75% of wild boars are captured-and-removed for at least three years showed long-term effectiveness for more than ten years. The current ASF control method, in which 33% of wild boars are captured-and-removed, decreased the number of wild boars for three years, after which the wild boar population increased to more than its initial number. Given the limited human and material resources for controlling ASF in the Republic of Korea, it is recommended that resources be prioritized to increase the capture-and-removal proportion of wild boars to take full advantage of the ASF-control effectiveness.

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.

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 purpose of this study is to obtain data on the zones formed by the movement of livestock vehicles and to determine if such areas can be used to establish quarantine activities and quarantine policies for livestock epidemics. For this purpose, this study used mobile data on poultry-related livestock vehicles generated in 2019. InfoMap, a community detection method, was used for regional classification, and the results of the analysis were visualized on a map using GIS. The study results confirmed that the zone of the administrative unit can be classified based on the movement of livestock vehicles. In addition, the zones created by the vehicle movement could be seen to change depending on the purpose and timing of the operation of livestock vehicles. Some areas form relatively stable zones, such as Jeolla-do and Gyeongsang-do, while others change depending on the situation, such as Chungcheong-do, Gyeonggi-do, and Gangwon-do. Further, the zones derived for poultry differed from those derived for cattle and pigs in previous studies.

This study examined the spatial autocorrelation of the 2016 foot and mouth disease (FMD) outbreaks in South Chungcheong to determine the association between the disease epidemics and pig farm vehicle movements. Two spatial autocorrelation testing methods were used: Moran’s I and Getis-Ord G statistics. The Moran’s I statistic for the FMD outbreak areas was -0.239, and its p-value was less than 0.007. The median Getis-Ord G statistic for the FMD outbreak areas was -0.323. The results indicated that the geographical distribution of the FMD outbreak areas was not spatially homogeneous. The spatial autocorrelation of the 2016 FMD epidemics was considered by applying a geographical weighted Poisson regression (GWPR) model in the analysis, in which pig farm vehicle movements were used as risk factors for the 2016 FMD epidemics. The number of FMD-infected farms per second-level administrative province (si or gun) was used as a dependent variable. The number of farm vehicle movements within the province (within variable), from one province to other provinces (outbound variable), or from other provinces to one province (inbound variable), were included as independent variables in the GWPR model. The results of the GWPR model were as follows. The estimated median coefficient of the log-transformed within variable, the log-transformed outbound variable, and log-transformed inbound variable were -0.000, 0.010, and -0.009, respectively. The optimal bandwidth for the GWPR model was 80.49, and the AIC score was 89.35. The results showed that the GWPR model would provide an understanding of the relationship between the 2016 FMD epidemics and pig farm vehicle movements

The distribution of wild boar (Sus scropa) in the Republic of Korea was forecasted using environmental factors. A species distribution model was applied with the standard normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), solar zenith angle (SUNZ), daytime land surface temperature (dTemp), and nighttime land surface temperature (nTemp). Understanding wild boar distribution is important for controlling African swine fever (ASF) because the disease could be endemic in wild boar or spread from wild boars to domestic pigs. Among the five predictors, the NDVI was the most influencing factor for the wild boar distribution. The relative contributions of the predictors were 67.4 for NDVI, 16.9 for dTemp, 10.5 for SUNZ, 4.4 for EVI, and 0.8 for nTemp. The area size under the receiver-operating curve of the receiver-operating characteristics for the current model was 0.62, but the real wild boar observation data overlapped with the predicted high-density wild boar distribution area. The wild boar distribution density was relatively higher in Gangwon-do, Gyeongsangbuk-do, Gyeongsangnam-do, and Jeollanam-do. Given the ASF epidemics, contact between ASF-infected animals and ASF-susceptible animals in high-density wild boar distribution areas should be prevented by long-range fencing or active surveillance.

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 purpose of this study was to find the change in social awareness in livestock stamping out over time. We used the text data mining method for this study. The data used in this analysis comprised approximately 5,600 newspaper articles. The data were sourced using the keywords 'livestock stamping out' 2010-2017, and classified into two-year intervals. The analysis showed a change in perspective on livestock disease and prevention activities over time. Additionally, it was confirmed that problems related to 'ethics' were continuously raised during the entire period. Prevention activities cannot succeed without the support and participation of people. Thus, the results of this study can be used to establish and implement livestock disease prevention policies.

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.

Epidemiological research to investigate the spatial characteristics of poultry farms confirmed with avian influenza (AI) infection can help increase the efficacy of AI surveillance as well as AI control strategies. The spatial characteristics of poultry farms confirmed with AI infection can provide insights on effective AI-surveillance and AI-control strategies to policymakers by providing a visualization of the geographical pattern of AI distribution. The goal of the current study was to investigate the spatial characteristics of the risk of a farm being AI-positive by using data from routine AI-surveillance performed during the period 2014–2015. To achieve this goal, we applied a spatial model because it improves the estimation of the relative risk by taking into account spatial dependence between epidemiological units. The results revealed there was a lack of dependency between districts in the risk of a farm being AI-positive. The estimates for the spatial autocorrelation coefficient in the spatial model for chicken farms were 0.006 in 2014 (p = 0.9496) and -0.064 in 2015 (p = 0.6052) and for duck farms were -0.066 in 2014 (p = 0.4380) and 0.047 in 2015. Likewise, Moran’s I statistic estimates for chicken farms were 0.0243 in 2014 (p = 0.3183) and -0.0174 in 2015 (p = 0.5657) and for duck farms were -0.0342 in 2014 (p = 0.6678) and -0.0230 in 2015.

The cost-effectiveness of foot-and-mouth disease (FMD) control strategies was evaluated using a simulation model fitted to the 2010/11 FMD epidemic in the city of Andong, Republic of Korea. Seven FMD-control strategies were evaluated with respect to the direct cost of a FMD-control strategy, such as slaughtering, movement restriction, and vaccination. All the strategies included pre-emptive slaughtering, movement restriction, and vaccination, but the levels of each control option were different. The simulated median cost of the baseline FMD-control strategy (three kilometers of pre-emptive slaughtering area, 100 days of movement restriction and vaccination of all FMD-susceptible animals in the study area) was estimated to be USD 99.7 million. When a five kilometer vaccination area was applied (with the other control measures being the same as the baseline strategy), the simulated median cost was reduced to USD 81.1 million from USD 99.7. The simulated median costs were USD 107.6 million for a five kilometer radius slaughtering area and USD 168.8 million for 60 days of movement restriction. The FMD-control strategy cost decreased with increasing number of farms depopulated per day. The probability of passive surveillance being effective or the probability of the successful implementation of movement restrictions were increased. Cost-effectiveness analysis is a suitable tool for evaluating the financial consequences of FMD-control strategies by comparing the cost of control strategies for a specific area.

A simulation model of the 2010/11 foot-and-mouth disease (FMD) epidemic in the city of Andong, Republic of Korea was constructed to evaluate the epidemiologic effectiveness of FMD-control strategies. Seven FMD-control strategies were evaluated with respect to a number of epidemiologic indicators relating to the outbreak, including the number of infected animals, number of infected farms, and epidemic duration. The FMD-control strategies in the model consisted of pre-emptive slaughtering, movement restriction, and vaccination; however, levels of each control option differed. The constructed model was not perfectly representative of the 2010/11 FMD epidemic, although it was considered to mimic the actual FMD epidemic in its prediction of two outcomes: the median number of simulated FMD-detected farms was 294 (range 207–515), which was close to the number of farms detected (299) during the actual FMD epidemic (x2=87.239, df=98, p = 0.774); and the simulated epidemic curve was visually similar to the actual epidemic curve of the 2010/11 FMD epidemic. The effectiveness evaluation of simulated FMD-control strategies emphasized the amount the FMD outbreak size could have increased if the radius of the pre-emptive slaughtering area or the duration of movement restriction were decreased.

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.