African swine fever (ASF) is a viral hemorrhagic fever fatal to animals of the Suidae family. It has spread from Africa to Europe and Asia, causing significant damage to wildlife and domesticated pig production. Since the first confirmed case in South Korea in September 2019, the number of infected wild boars has continued to increase, despite quarantine fences and hunting operations. Hence, new strategies are needed for the effective control of ASF. We developed an agent-based model (ABM) to estimate the ASF expansion area and the efficacy of infection control strategies. In addition, we simulated the agents’ (wild boars) behavior and daily movement range based on their ecological and behavioral characteristics, by applying annual hunting scenarios from past three years (2019.09–2022.08). The results of the simulation based on the annual changes in the number of infected agents and the ASF expansion area showed that the higher the hunting intensity, the smaller the expansion area (24,987 km² at 0% vs. 3533 km² at 70%); a hunting intensity exceeding 70% minimally affected the expansion area. A complete removal of agents during the simulation period was shown to be possible. In conclusion, an annual hunting intensity of 70% should be maintained to effectively control ASF.

African swine fever (ASF) is a highly contagious viral disease affecting both domestic and wild boars. Since its first outbreak in South Korea in 2019, substantial efforts have been made to prevent ASF transmission by reducing the wild boar population and eliminating infected carcasses; however, the persistence of ASF transmission has posed challenges to these efforts. To improve ASF management strategies, the limitations of current management strategies must be identified by considering disparities between wild boar habitats and ASF-managed areas with environmental and anthropogenic characteristics of wild boars and their management strategies. Here, ensemble species distribution models were used to estimate wild boar habitats and potential ASF-managed areas, with elevation, distance to urban areas, and Normalized Difference Vegetation Index as important variables. Binary maps of wild boar habitats and potential ASF-managed areas were generated using the maxSSS as the threshold criterion. Disparity areas of ASF management were identified by overlying regions evaluated as wild boar habitats with those not classified as ASF-managed areas. Dense forests near urban regions like Chungcheongbuk-do, Gyeongsangbuk-do, and Gyeongsangnam-do were evaluated as disparity areas having high risk of ASF transmission. These findings hold significant potential for refining ASF management strategies and establishing proactive control measures.

African swine fever (ASF) is a transboundary viral disease causing severe impacts on national animal health systems in wild and domestic suids. Since its official report in North Korea in 2019, ASF has posed a persistent threat to livestock production, public health, and ecological safety on the Korean Peninsula. Wild boars are recognized as a key reservoir and vector facilitating long-distance spread of ASF, particularly across national borders. However, in North Korea, critical information on outbreak locations, wild boar population density, and transmission pathways remains inaccessible, making risk assessment and preparedness extremely challenging. This study aims to estimate the potential origin and spatial and temporal spread of ASF in North Korea, despite severe data limitations, by following the method from Ko and Cho et al. (2023), applying an agent-based modeling (ABM) and machine-learning framework. In the model, we simulate the wild boar migration and ASF virus transmission. Wild boar sounders were represented as agents whose movement, social structure, reproduction, and contact behaviors were parameterized using ecological and physiological information from the literature-based database. ASF transmission was simulated through local contacts among agents in a spatially explicit landscape, and infection trajectories were tracked over time to estimate transmission pathways and the timing of potential arrival at the Demilitarized Zone (DMZ). Two introduction scenarios were examined based on proximity to reported outbreaks in northeastern China and prior epidemiological evidence: Usi County in Jagang Province, the only officially reported outbreak site in North Korea (Scenario 01), and Hoeryong City in North Hamgyong Province, where suspected early mortality events were reported (Scenario 02). Repeated simulations were conducted for each scenario to identify dominant spread patterns and temporal dynamics. While Scenario 01 successfully reproduces the large-scale southward diffusion pattern toward the DMZ, and Scenario 02 remains constrained mainly by topography, the model fails to capture the short elapsed time from the emergence of ASF in North Korea to its arrival at the DMZ in 2019. This temporal mismatch indicates that, although wild boar-driven spatial spread is plausibly represented, additional mechanisms—such as human-mediated long-distance transmission, earlier widespread circulation before official reporting, or multiple introductions including trade-related pathways—are likely required to explain the observed dynamics. Overall, this study demonstrates how agent-based modeling can be used to reconstruct plausible disease spread scenarios in data-scarce regions and provides insights for prioritizing transboundary surveillance and control strategies along the Korean DMZ.

Island regions serve as critical ecological functions, maintaining unique biodiversity and endemic species. Due to their geographical isolation, island ecosystems are exceptionally vulnerable to external anthropogenic and natural disturbances, including overgrazing by wildlife and feral livestock, land-use changes, and the impacts of climate change. By this susceptibility, establishing precise quantification and configuration of deforestation and restoration are essential for the sustainable management of island regions. This study aims to identify and validate core remote sensing indicators capable of detecting long-term deforestation and restoration efforts across diverse island landscapes in South Korea. A comprehensive island library of deforestation and recovery was constructed by synthesizing historical literature and field reports. This library contains a list of deforestation and restoration islands, disturbance types, and disturbed periods. To evaluate spatio-temporal dynamics in island landscape, we utilized multi-temporal satellite imagery and estimated three indicators to represent the vital and biophysical conditions of island ecosystems: Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Inverted Tasseled Cap Wetness-Greenness Difference (TCWGDinv). The analytical framework employed Sen’s slope to determine the magnitude of monotonic trends in each indicators, to provide a robust rate of degradation or recovery over time. To ensure the statistical significance, the Mann-Kendall trend test was conducted, allowing for a rigorous spatial assessment of areas undergoing significant ecological dynamics. The study focused on four representative island sites: Two island sites, Guleop-do island and Anma-do island, damaged from the intense browsing by the ungulate, one island site, Geoje-do island, damaged by pests, and one active restoration site, Wonsan-do island. All three indicators consistently detected significant decreasing trends in deforestation areas, effectively quantifying the reduction of vital of tree species. In the restoration site, NDVI and SAVI showed increased trends over time, efficiently detecting successful restoration. However, TCWGDinv demonstrated inverse trend, likely due to the high ambient soil moisture characteristics inherent to island regions. This study demonstrates that while the integration of these three indicators provides a useful tool for monitoring forest degradation and quantifying damage, the application of moisture-based indices like TCWGDinv for assessing restoration requires careful calibration according to site-specific environmental variables. These results provide a scientific foundation for developing optimized, data-driven strategies for the long-term conservation and ecological restoration of vulnerable island forest ecosystems.