Spatial epidemiology of lumpy skin disease: unraveling patterns in dairy farm clusters with short interfarm proximity

Abstract

Lumpy skin disease (LSD) has caused economic losses in cattle, and Thailand experienced a nationwide outbreak in 2021. Spatial epidemiology plays a crucial role in identifying transmission patterns and high-risk areas for targeted disease control. This study examines the spatial epidemiology of LSD by analyzing clustering patterns, disease hotspots, and the directional spread of outbreaks in dairy farm networks with short interfarm proximities. LSD outbreak data from a large dairy farming area in northern Thailand were analyzed via multiple spatial analytical techniques. The standard deviation ellipse (SDE) approach, implemented with the Yuill and CrimeStat methods, was employed to determine the spatial-directional spread of outbreaks. Global and local Moran’s I statistics were used to assess spatial autocorrelation, whereas kernel density estimation (KDE) was used to identify the density areas of the LSD outbreaks. Ordinary kriging was applied to interpolate high-intensity surfaces. The results from the SDE indicate that the LSD outbreaks predominantly followed a northeast-to-southwest trend. Global Moran’s I revealed no statistical significance, whereas local Moran’s I indicated significant local spatial autocorrelation. KDE revealed a high density of outbreaks in the upper northern part of the farming region. Additionally, ordinary kriging was used to quantify the likelihood of outbreaks across different areas, highlighting potential high-intensity surfaces. These results enhance the understanding of LSD spatial epidemiology, providing valuable insights into disease dynamics and transmission. Additionally, these findings support policymakers in making informed decisions on targeted prevention, control strategies, and resource allocation at the local and regional levels.

Keywords

Cluster / Hotspot area / Lumpy skin disease / Spatial epidemiology

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Funding

Chiang Mai University

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