[C07] Infectious Diseases

[C07-03] Spatiotemporal Risk Assessment and Intervention Simulation for African Swine Fever: A Data Driven Approach in the Republic of Korea

*Yongin Choi¹, Changdae Son², Kyeong Tae Ko², Janghun Oh², Hyojung Lee² (1. Research Institute of Applied Statistics, Sungkyunkwan University (Korea), 2. Department of Statistics, Kyungpook National University (Korea))

Keywords:

African swine fever,Spatiotemporal transmission,Statistical modeling,Stochastic simulation,Risk cluster

African Swine Fever (ASF) is a highly contagious and fatal disease that affects both domestic pigs and wild boars, with continued reported in the Republic of Korea since its first detection in 2019. Given its nearly 100% mortality rate and the absence of effective vaccines or treatments, ASF presents a significant threat to the swine industry, particularly through the potential spillover from wild boars to domestic pig populations. Effective disease control requires a comprehensive understanding of its transmission dynamics and spatial risk distribution.To support ASF and enhance ASF prevention strategies, we developed two complementary modeling approaches that integrate empirical surveillance data with ecological behavior. First, we constructed a statistical model to identify spatiotemporal transmission patterns and detect high-risk clusters, using nationwide surveillance data on ASF-infected wild boar carcasses. By accounting for observation errors and varying surveillance intensities, the model allowed for more accurate identification of regional ASF risk. In parallel, we developed a mathematical modeling framework to simulate the spatiotemporal spread of ASF, incorporating wild boar movement patterns and transmission characteristics. Movement probabilities were derived from species distribution models (SDMs), which quantified habitat suitability based on environmental and socio-ecological factors. These probabilities were integrated into the model along with carcass-mediated transmission, wild boar population dynamics, and intervention measures such as fencing and hunting. The model was calibrated using real outbreak data and applied to evaluate the effectiveness of various intervention scenarios.By combining data-driven statistical analysis with behavior-based transmission modeling, our study provides valuable insights into ASF transmission mechanisms and offers practical guidance for improving targeted surveillance and control strategies in Korea.