Spatial epidemiology of lumpy skin disease outbreaks in Thailand: proximity patterns, case concentrations, spatio-temporal clusters, and interpolated case distribution.

内容摘要

Lumpy skin disease (LSD) is an emerging transboundary disease affecting cattle and buffaloes, causing substantial economic and animal health consequences. Understanding its spatial dynamics is essential for formulating effective control and prevention strategies. This study aimed to characterize the spatial distribution of LSD outbreaks, identify significant outbreak clusters and interpolate the spatial distribution of LSD case values intensity across Thailand. Data on LSD outbreaks in Thailand between March 2021 and December 2021 were analyzed. Nearest neighbor analysis was employed to assess the spatial proximity among outbreak locations, while weighted kernel density estimation (KDE) was used to identify geographic areas with high concentrations of LSD cases. Additionally, space-time clusters were detected using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm combined with the Knox method. Universal kriging was performed to interpolate the spatial distribution of reported LSD case values while accounting for cattle population and spatial dependence. The results demonstrated that the mean nearest-neighbor distance between outbreak sites was 13.75 km. The KDE identified areas with high concentrations of reported LSD cases in northeastern and west-central Thailand, whereas southern and peripheral regions exhibited lower densities. The DBSCAN detected eight space-time clusters, with prominent clusters located in the northeastern, northern, and central regions. The universal kriging provided a continuous interpolation of LSD case values and revealed elevated interpolate values in the northeastern and western parts of the northern region. This study combined multiple geospatial approaches, including exploratory mapping, density estimation, spatial interpolation, and space-time cluster detection, to analyze LSD outbreak data. These spatial epidemiological insights provide explicit risk estimates and are essential for guiding efficient resource allocation, informing targeted intervention strategies, and strengthening disease control efforts in high-risk regions.