Seasonal dynamics of recurrent epidemics
Seasonal dynamics of recurrent epidemics
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DOI：
10.1038/nature05638
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发表时间：
2007-03-29
期刊：
影响因子：
64.8
通讯作者：
Huppert, Amit
中科院分区：
文献类型：
--
作者：
Stone, Lewi;Olinky, Ronen;Huppert, Amit
季节性是对自然系统及其种群的时空动态有重大影响的驱动力(1-5)。对于常见传染病(如流感、麻疹、水痘和百日咳)的传播尤其如此，而且与宿主-寄生虫的一般关系密切相关(1-23)。在这里，我们通过分析经典的季节性强迫SIR(易感、传染性或恢复期)流行病模型(6，7)，进一步深入了解复发疾病的非线性动力学。我们的分析与其他建模研究的不同之处在于，重点更多地放在疫情后的动态上，而不是疫情本身。尽管强迫SIR模型在数学上很难处理，但我们确定了一个新的阈值效应，并给出了明确的分析条件，用于预测未来疫情暴发的发生，或预测“跳跃”--一种流行病未能启动的一年。这一阈值是由上次暴发后测量的人群易感性以及新的易感人群被招募到人群中的速度确定的。此外，暴发的发生时间(即阶段)被证明是携带重要流行病学信息的有用参数。在强制系统中，季节变化可以防止高峰期疾病(即高峰期疾病)广泛传播，从而增加人口易感性，并控制未来流行病的触发和强度。这些原则产生了预测工具，这些工具应该与季节性病媒控制的新出现和重新出现的疾病的研究相关。
Seasonality is a driving force that has a major effect on the spatiotemporal dynamics of natural systems and their populations(1-5). This is especially true for the transmission of common infectious diseases ( such as influenza, measles, chickenpox and pertussis), and is of great relevance for host-parasite relationships in general(1-23). Here we gain further insights into the nonlinear dynamics of recurrent diseases through the analysis of the classical seasonally forced SIR ( susceptible, infectious or recovered) epidemic model(6,7). Ouranalysis differs from other modelling studies in that the focus is more on post-epidemic dynamics than the outbreak itself. Despite the mathematical intractability of the forced SIR model, we identify a new threshold effect and give clear analytical conditions for predicting the occurrence of either a future epidemic outbreak, or a 'skip'-a year in which an epidemic fails to initiate. The threshold is determined by the population's susceptibility measured after the last outbreak and the rate at which new susceptible individuals are recruited into the population. Moreover, the time of occurrence ( that is, the phase) of an outbreak proves to be a useful parameter that carries important epidemiological information. In forced systems, seasonal changes can prevent late-peaking diseases ( that is, those having high phase) from spreading widely, thereby increasing population susceptibility, and controlling the triggering and intensity of future epidemics. These principles yield forecasting tools that should have relevance for the study of newly emerging and re-emerging diseases controlled by seasonal vectors.