Savini L, Candeloro L, Conte A, De Massis F, Giovannini A
Istituto Zooprofilattico Sperimentale dell'Abruzzo e Molise, National and OIE Reference Laboratory for Brucellosis, OIE Collaborating Centre for Veterinary Training, Epidemiology, Food Safety and Animal Welfare, Via Campo Boario, Teramo, Italy.
PLoS One. 2017 Jun 27;12(6):e0177313. doi: 10.1371/journal.pone.0177313. eCollection 2017.
Brucellosis caused by Brucella abortus is an important zoonosis that constitutes a serious hazard to public health. Prevention of human brucellosis depends on the control of the disease in animals. Livestock movement data represent a valuable source of information to understand the pattern of contacts between holdings, which may determine the inter-herds and intra-herd spread of the disease. The manuscript addresses the use of computational epidemic models rooted in the knowledge of cattle trade network to assess the probabilities of brucellosis spread and to design control strategies. Three different spread network-based models were proposed: the DFC (Disease Flow Centrality) model based only on temporal cattle network structure and unrelated to the epidemiological disease parameters; a deterministic SIR (Susceptible-Infectious-Recovered) model; a stochastic SEIR (Susceptible-Exposed-Infectious-Recovered) model in which epidemiological and demographic within-farm aspects were also modelled. Containment strategies based on farms centrality in the cattle network were tested and discussed. All three models started from the identification of the entire sub-network originated from an infected farm, up to the fifth order of contacts. Their performances were based on data collected in Sicily in the framework of the national eradication plan of brucellosis in 2009. Results show that the proposed methods improves the efficacy and efficiency of the tracing activities in comparison to the procedure currently adopted by the veterinary services in the brucellosis control, in Italy. An overall assessment shows that the SIR model is the most suitable for the practical needs of the veterinary services, being the one with the highest sensitivity and the shortest computation time.
由流产布鲁氏菌引起的布鲁氏菌病是一种重要的人畜共患病，对公众健康构成严重危害。预防人类布鲁氏菌病依赖于控制动物中的该疾病。牲畜流动数据是了解养殖场之间接触模式的宝贵信息来源，这种接触模式可能决定疾病在畜群间和畜群内的传播。本文探讨了基于牛贸易网络知识的计算性流行病模型的应用，以评估布鲁氏菌病传播的可能性并设计控制策略。提出了三种不同的基于传播网络的模型：仅基于牛群时间网络结构且与疾病流行病学参数无关的DFC（疾病流中心性）模型；确定性SIR（易感-感染-康复）模型；一种随机SEIR（易感-暴露-感染-康复）模型，其中还对农场内部的流行病学和人口统计学方面进行了建模。测试并讨论了基于牛群网络中养殖场中心性的遏制策略。所有这三种模型均从识别源自感染养殖场的整个子网络开始，直至第五级接触。它们的性能基于2009年在西西里岛国家布鲁氏菌病根除计划框架内收集的数据。结果表明，与意大利兽医服务部门目前在布鲁氏菌病控制中采用的程序相比，所提出的方法提高了追踪活动的有效性和效率。总体评估表明，SIR模型最适合兽医服务的实际需求，因为它具有最高的敏感性和最短的计算时间。
