Prentice Jamie C, Marion Glenn, Hutchings Michael R, McNeilly Tom N, Matthews Louise
Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, Glasgow G61 1QH, UK
Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK.
J R Soc Interface. 2017 Jan;14(126). doi: 10.1098/rsif.2016.0531.
Livestock disease controls are often linked to movements between farms, for example, via quarantine and pre- or post-movement testing. Designing effective controls, therefore, benefits from accurate assessment of herd-to-herd transmission. Household models of human infections make use of R, the number of groups infected by an initial infected group, which is a metapopulation level analogue of the basic reproduction number R that provides a better characterization of disease spread in a metapopulation. However, existing approaches to calculate R do not account for individual movements between locations which means we lack suitable tools for livestock systems. We address this gap using next-generation matrix approaches to capture movements explicitly and introduce novel tools to calculate R in any populations coupled by individual movements. We show that depletion of infectives in the source group, which hastens its recovery, is a phenomenon with important implications for design and efficacy of movement-based controls. Underpinning our results is the observation that R peaks at intermediate livestock movement rates. Consequently, under movement-based controls, infection could be controlled at high movement rates but persist at intermediate rates. Thus, once control schemes are present in a livestock system, a reduction in movements can counterintuitively lead to increased disease prevalence. We illustrate our results using four important livestock diseases (bovine viral diarrhoea, bovine herpes virus, Johne's disease and Escherichia coli O157) that each persist across different movement rate ranges with the consequence that a change in livestock movements could help control one disease, but exacerbate another.
牲畜疾病控制通常与农场间的动物移动相关，例如，通过检疫以及移动前或移动后的检测。因此，设计有效的控制措施需要准确评估畜群间的传播情况。人类感染的家庭模型使用R，即由初始感染群体感染的群体数量，它是基本繁殖数R在集合种群水平上的类似物，能更好地描述疾病在集合种群中的传播。然而，现有的计算R的方法没有考虑地点之间的个体移动，这意味着我们缺乏适用于牲畜系统的工具。我们使用下一代矩阵方法来明确捕捉移动情况，从而填补这一空白，并引入新颖的工具来计算任何由个体移动连接的种群中的R。我们表明，源群体中感染个体的减少会加速其恢复，这一现象对基于移动的控制措施的设计和效果具有重要影响。我们的研究结果基于这样的观察：R在中等牲畜移动率时达到峰值。因此，在基于移动的控制措施下，感染在高移动率时可以得到控制，但在中等移动率时会持续存在。所以，一旦牲畜系统中存在控制方案，移动的减少可能会导致疾病患病率反而增加，这与直觉相反。我们用四种重要的牲畜疾病（牛病毒性腹泻、牛疱疹病毒、副结核和大肠杆菌O157）来说明我们的研究结果，这四种疾病在不同的移动率范围内都持续存在，结果是牲畜移动的变化可能有助于控制一种疾病，但会加剧另一种疾病。
