Matsumoto Nina, Halasa Tariq, Schemann Kathrin, Khounsy Syseng, Douangngeun Bounlom, Thepagna Watthana, Phommachanh Phouvong, Siengsanan-Lamont Jarunee, Young James, Toribio Jenny-Ann, Bush Russell, Blacksell Stuart D, Ward Michael P
Sydney School of Veterinary Science, The University of Sydney, Camden, NSW, Australia.
Section of Animal Welfare and Disease Control, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Trop Anim Health Prod. 2025 May 10;57(4):216. doi: 10.1007/s11250-025-04443-2.
Understanding the spread of African Swine Fever (ASF) between villages in the southeast-Asian, low - middle income country context is critical if this high impact disease is to be controlled by good policy and effective field activities in these resource-poor settings. Using governmental reporting data from the 2019 outbreak of ASF in Lao People's Democratic Republic, spatial clustering techniques were used to identify clusters of outbreak villages. Then Approximate Bayesian Computation with Sequential Monte Carlo was used to estimate the transmission parameters of ASF virus between the villages within these clusters. We used a simple disease spread model to understand the impact of parameter estimation on predicted disease spread and thus decision-making. Six clusters of radius 16 to 153km were identified over the 7 month outbreak period. Within these clusters, the basic reproduction number (R) ranged from 13 to 32 between-villages and whole-village infectious periods ranged from 62 to 68 days. The final model outputs were compared to the original field report data. We found that the ability of the estimated parameters to match field data was heavily reliant on how the original field surveillance data was reported. Specifically, in situations in which cases in a cluster appeared to have been reported as batches (lack of temporal specificity) our modelling approach failed to produce satisfactory outputs in terms of model fit and precision of estimates. This study demonstrates that surveillance for transboundary diseases not only has immediate benefit for disease response, but that good quality surveillance data is valuable for informing future planning for disease response via appropriately parameterised disease spread models. There is a need for ongoing quality control of surveillance and support for field veterinary services to ensure quality data that can be used to drive policy and decision-making.
在东南亚中低收入国家的背景下，若要通过这些资源匮乏地区的良好政策和有效的实地活动来控制非洲猪瘟（ASF）这种具有高影响力的疾病，了解其在村庄之间的传播情况至关重要。利用老挝人民民主共和国2019年ASF疫情的政府报告数据，采用空间聚类技术来识别疫情爆发村庄的集群。然后使用带有序贯蒙特卡罗的近似贝叶斯计算方法来估计这些集群内村庄之间ASF病毒的传播参数。我们使用一个简单的疾病传播模型来了解参数估计对预测疾病传播以及决策的影响。在7个月的疫情爆发期内，识别出了半径为16至153公里的6个集群。在这些集群中，村庄之间的基本再生数（R）在13至32之间，整个村庄的感染期在62至68天之间。将最终的模型输出与原始实地报告数据进行了比较。我们发现，估计参数与实地数据匹配的能力在很大程度上依赖于原始实地监测数据的报告方式。具体而言，在一个集群中的病例似乎是按批次报告的情况（缺乏时间特异性）下，我们的建模方法在模型拟合和估计精度方面未能产生令人满意的输出。这项研究表明，跨境疾病监测不仅对疾病应对有直接益处，而且高质量的监测数据对于通过适当参数化的疾病传播模型为未来疾病应对规划提供信息很有价值。需要持续对监测进行质量控制，并为实地兽医服务提供支持，以确保可用于推动政策和决策的高质量数据。
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