Space and contact networks: capturing the locality of disease transmission
Space and contact networks: capturing the locality of disease transmission
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DOI：
10.1098/rsif.2005.0105
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发表时间：
2006-08-22
影响因子：
3.9
通讯作者：
Ferguson, Neil M.
中科院分区：
文献类型：
--
作者：
Parham, Paul E.;Ferguson, Neil M.
虽然空间流行病的模拟可能包含任意程度的复杂性，但计算强度和分析难度意味着这种模型往往对流行病动态的决定因素缺乏透明度。虽然许多方法试图解决这种复杂性的易处理性权衡，矩封闭方法可以说提供了最有前途的和强大的框架，捕捉全球疾病动态的接触过程的局部性的作用。虽然完全随机空间传播模型和动态网络模型之间可以进行密切的类比，但我们在这里考虑的特殊情况是，网络拓扑结构的动态变化在时间尺度上比施加在它们上的流行病学过程长得多;在这种情况下，使用静态网络模型是合理的。我们表明，在这种情况下，静态网络模型可以提供良好的近似的潜在的空间接触过程，通过适当选择的有效邻域大小。我们还证明了这种映射的鲁棒性，通过检查确定性近似的等价性，三阶矩封闭假设下得出的完整的空间和网络模型。对于偏离均匀混合的系统是有限的，我们表明，对网络模型的方程至少是一个很好的近似基础随机空间模型作为更复杂的空间矩方程，这两类近似变得不太准确，只有高度本地化的内核。
While an arbitrary level of complexity may be included in simulations of spatial epidemics, computational intensity and analytical intractability mean that such models often lack transparency into the determinants of epidemiological dynamics. Although numerous approaches attempt to resolve this complexity tractability trade-off,moment closure methods arguably offer the most promising and robust frameworks for capturing the role of the locality of contact processes on global disease dynamics. While a close analogy may be made between full stochastic spatial transmission models and dynamic network models, we consider here the special case where the dynamics of the network topology change on time-scales much longer than the epidemiological processes imposed on them; in such cases, the use of static network models are justified. We show that in such cases, static network models may provide excellent approximations to the underlying spatial contact process through an appropriate choice of the effective neighbourhood size. We also demonstrate the robustness of this mapping by examining the equivalence of deterministic approximations to the full spatial and network models derived under third-order moment closure assumptions. For systems where deviation from homogeneous mixing is limited, we show that pair equations developed for network models are at least as good an approximation to the underlying stochastic spatial model as more complex spatial moment equations, with both classes of approximation becoming less accurate only for highly localized kernels.