Abstract
Since African swine fever (ASF) was confirmed in South Korea in September 2019, the disease keeps spreading primarily in wild boars. The key element contributing to this situation is the lack of identification of risk factors lead by surveillance bias. The high-elevation mountainous terrain covering 70% of the country makes search challenging. Inadequate application of surveillance programs to this unique orography results in missed cases and also poses a threat to the swine industry. Identifying potential under-surveillance areas would contribute to early detection of disease and improve the current surveillance system.
The directional distribution analysis was first performed to characterize the spatiotemporal distribution trends of ASF-positive wild boars. Then combinations of geospatial analysis and statistical approaches were applied to estimating potential under-surveillance areas. For South Korea, another important surveillance subject is the river shared with North Korea in terms of the continuous source of the virus introduction. While these countries are separated by the demilitarized zone serving as a physical barrier to the wild boar's movement, several rivers spanning both countries allow for the influx of infectious agents. The statistical analysis was performed to determine the relationship between ASF-positive wild boars and water, and then hydrologic analysis was applied to identify points for monitoring the risk of ASF spread.
The directional distribution analysis indicated a gradual shift in the center of ASF occurrence to the southeast, most likely due to the wild boar’s movement. At the same time, several groups possibly infected by other routes were observed in distant areas.
Pearson's rho test indicated that elevation (rho= -0.908, p-value < 0.001) and distance from roads (rho= -0.979, p-value < 0.001) may have a significant impact on limiting surveillance activities, while the quality of available habitat for wild boar (QAH) map was shown to be useful in South Korea to indicate the probability of wild boar presence (rho = 0.786, p-value = 0.036). The map of potential under-surveillance areas was created considering these results, and was validated by a chi-square goodness-of-fit test (X-squared = 208.03, df = 1, p-value < 0.001).
The strong negative correlation between ASF-positive wild boars and distance from water sources (rho= -0.997, p-value <0.001) suggests that areas around rivers are one of the priority areas for monitoring. Furthermore, the hydrological analysis provided strategic surveillance points to monitor the risk of ASF spreading through the water inside South Korea as well as the continuous risk of ASF entry from North Korea.
In this study, we have developed tools to improve surveillance activities for the early detection and warming system of ASF. The identification of potential under-surveillance areas as well as missed risk factors can greatly improve monitoring systems. Thus, more effective and efficient countermeasures can be implemented.