Vulnerability and epidemic spread on the Tongyeong coast: A spatiotemporal hybrid model comparison of disease transmission scenarios in aquaculture

Abstract

The sea cage farming system is one of the most common methods used for culturing fin-fish in South Korea. The region where these fish have been cultured most intensively using sea-cages is the Tongyeong coast. However, assessments of the risk of disease transmission on the Tongyeong coast that consider multiple epidemiological factors have, to date, not been sufficiently addressed. To address this, the current study utilized a spatiotemporal hybrid model to evaluate the risk of infectious disease transmission under various scenarios in fish farms along the Tongyeong coast. Based on their geographical location, 87 fish farms were divided into three bay management areas, A, B, and C (BMA A-C). To evaluate the vulnerability of disease transmission based on the location of each fish farm, 2175 disease outbreak simulations were conducted using different ScalingInf that reflect the chance of widespread disease transmission. The spread of disease along the Tongyeong coast was then predicted under different ScalingInf values and different host susceptibility for outbreaks occurring in each fish farm belonging to one of the three BMAs. The findings revealed that the central region of fish farm BMA A exhibited a 2 to 10 times higher likelihood of disease transmission in the worst-case scenario, highlighting its vulnerability compared with other areas. Overall, the extent of disease spread varied depending on where the outbreak occurred and the ScalingInf values. Outbreaks at fish farms in BMA A or B significantly impacted most farms within BMAs A and B, whereas those in BMA C had a relatively minor effect. Likewise, we found that differences in host susceptibility can significantly impact disease transmission among fish farms. Notwithstanding the limitations of the model, such as its inability to fully account for vector-based transmission and complex interactions between pathogens, hosts, and environmental factors, it provides valuable insights that can be used for predicting susceptible regions and optimizing fish farm management. © 2025 Elsevier B.V.