Ko Kyeong Tae, Oh Janghun, Son Changdae, Choi Yongin, Lee Hyojung
Department of Statistics, Kyungpook National University, Daegu, Republic of Korea.
Busan Center for Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
Front Vet Sci. 2024 Jul 24;11:1416862. doi: 10.3389/fvets.2024.1416862. eCollection 2024.
African swine fever (ASF) is a disease with a high mortality rate and high transmissibility. Identifying high-risk clusters and understanding the transmission characteristics of ASF in advance are essential for preventing its spread in a short period of time. This study investigated the spatial and temporal heterogeneity of ASF in the Republic of Korea by analyzing surveillance data on wild boar carcasses.
We observed a distinct annual propagation pattern, with the occurrence of ASF-infected carcasses trending southward over time. We developed a rank-based statistical model to evaluate risk by estimating the average weekly number of carcasses per district over time, allowing us to analyze and identify risk clusters of ASF. We conducted an analysis to identify risk clusters for two distinct periods, Late 2022 and Early 2023, utilizing data from ASF-infected carcasses. To address the underestimation of risk and observation error due to incomplete surveillance data, we estimated the number of ASF-infected individuals and accounted for observation error via different surveillance intensities.
As a result, in Late 2022, the risk clusters identified by observed and estimated number of ASF-infected carcasses were almost identical, particularly in the northwestern Gyeongbuk region, north Chungbuk region, and southwestern Gangwon region. In Early 2023, we observed a similar pattern with numerous risk clusters identified in the same regions as in Late 2022.
This approach enhances our understanding of ASF spatial dynamics. Additionally, it contributes to the epidemiology and study of animal infectious diseases by highlighting areas requiring urgent and focused intervention. By providing crucial data for the targeted allocation of resources for disease management and preventive measures, our findings lay vital groundwork for improving ASF management strategies, ultimately aiding in the containment and control of this devastating disease.
非洲猪瘟（ASF）是一种死亡率高且传播性强的疾病。提前识别高风险集群并了解ASF的传播特征对于在短时间内防止其传播至关重要。本研究通过分析野猪尸体监测数据，调查了韩国ASF的时空异质性。
我们观察到一种明显的年度传播模式，随着时间的推移，ASF感染尸体的出现呈向南趋势。我们开发了一种基于秩的统计模型，通过估计各地区随时间变化的每周平均尸体数量来评估风险，从而使我们能够分析和识别ASF的风险集群。我们利用ASF感染尸体的数据，对2022年末和2023年初这两个不同时期进行了风险集群分析。为了解决由于监测数据不完整导致的风险低估和观察误差问题，我们估计了ASF感染个体的数量，并通过不同的监测强度来考虑观察误差。
结果显示，在2022年末，通过观察到的和估计的ASF感染尸体数量确定的风险集群几乎相同，特别是在庆北地区西北部、忠北地区北部和江原道地区西南部。在2023年初，我们观察到了类似的模式，在与2022年末相同的地区发现了许多风险集群。
这种方法增强了我们对ASF空间动态的理解。此外，它通过突出需要紧急和重点干预的领域，为动物传染病的流行病学和研究做出了贡献。通过为疾病管理和预防措施的资源定向分配提供关键数据，我们的研究结果为改进ASF管理策略奠定了重要基础，最终有助于遏制和控制这种毁灭性疾病。
