Churchill College, University of Cambridge, Cambridge, UK.
J R Soc Interface. 2012 May 7;9(70):949-56. doi: 10.1098/rsif.2011.0506. Epub 2011 Nov 2.
There is increasing interest in the use of the percolation paradigm to analyse and predict the progress of disease spreading in spatially structured populations of animals and plants. The wider utility of the approach has been limited, however, by several restrictive assumptions, foremost of which is a strict requirement for simple nearest-neighbour transmission, in which the disease history of an individual is influenced only by that of its neighbours. In a recent paper, the percolation paradigm has been generalized to incorporate synergistic interactions in host infectivity and susceptibility, and the impact of these interactions on the invasive dynamics of an epidemic has been demonstrated. In the current paper, we elicit evidence that such synergistic interactions may underlie transmission dynamics in real-world systems by first formulating a model for the spread of a ubiquitous parasitic and saprotrophic fungus through replicated populations of nutrient sites and subsequently fitting and testing the model using data from experimental microcosms. Using Bayesian computational methods for model fitting, we demonstrate that synergistic interactions are necessary to explain the dynamics observed in the replicate experiments. The broader implications of this work in identifying disease-control strategies that deflect epidemics from invasive to non-invasive regimes are discussed.
人们越来越关注利用渗流范式来分析和预测动植物空间结构种群中疾病传播的进展。然而，该方法的广泛应用受到了几个限制性假设的限制，其中最主要的假设是严格要求采用简单的最近邻传播，即个体的疾病史仅受其邻居的影响。在最近的一篇论文中，渗流范式已经被推广到包含宿主易感性和感染性的协同作用，并且已经证明了这些相互作用对传染病流行的入侵动态的影响。在当前的论文中，我们通过首先为通过营养位点复制种群传播的普遍寄生和腐生真菌制定模型，然后使用来自实验微宇宙的数据拟合和测试模型，来证明这种协同作用可能是真实系统中传播动态的基础。我们使用贝叶斯计算方法进行模型拟合，证明了协同作用对于解释在重复实验中观察到的动态是必要的。讨论了这项工作在确定使流行病从入侵状态转向非入侵状态的疾病控制策略方面的更广泛意义。
