The basic reproduction number for complex disease systems:: Defining R0 for tick-borne infections
The basic reproduction number for complex disease systems:: Defining R0 for tick-borne infections
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DOI：
10.1086/587530
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发表时间：
2008-06-01
影响因子：
2.9
通讯作者：
Heesterbeek, J. A. P.
中科院分区：
文献类型：
--
作者：
Hartemink, N. A.;Randolph, S. E.;Heesterbeek, J. A. P.
描述许多野生动物疾病系统的基本繁殖数R-0似乎是一个复杂的问题，因为涉及到几个物种，因为在不同的生活史阶段对感染剂有不同的流行病学反应，或者因为有多种传播途径。蜱传疾病是一个重要的例子，所有这些复杂性都是由于蜱虫生命周期的特殊性和发生的多种传播途径而聚集在一起的。我们在这里表明，可以克服这些复杂性，通过分离的主机人口流行病学不同类型的个人和构建一个矩阵的繁殖数，所谓的下一代矩阵。每个矩阵元素是由第二类型的单个感染性个体产生的一种类型的感染性个体的预期数量。该矩阵的最大特征值表征了感染者数量的初始指数增长或下降。因此，低于1的值意味着感染不能建立。生物学解释与只有一种类型的个体和R-0的疾病系统的解释非常吻合，其中感染是直接传播的。定义每个矩阵元素的参数具有明确的生物学意义。我们说明的实用性和权力的方法与蜱传疾病的详细检查，我们使用现场和实验数据参数化的下一代矩阵莱姆病和蜱传脑炎。矩阵的敏感性和弹性分析，在元素和单个参数水平，允许直接比较两种病原体。这进一步支持了共食蜱之间的传播对于蜱传脑炎的建立至关重要。
Characterizing the basic reproduction number, R-0, for many wildlife disease systems can seem a complex problem because several species are involved, because there are different epidemiological reactions to the infectious agent at different life-history stages, or because there are multiple transmission routes. Tick-borne diseases are an important example where all these complexities are brought together as a result of the peculiarities of the tick life cycle and the multiple transmission routes that occur. We show here that one can overcome these complexities by separating the host population into epidemiologically different types of individuals and constructing a matrix of reproduction numbers, the so-called next-generation matrix. Each matrix element is an expected number of infectious individuals of one type produced by a single infectious individual of a second type. The largest eigenvalue of the matrix characterizes the initial exponential growth or decline in numbers of infected individuals. Values below 1 therefore imply that the infection cannot establish. The biological interpretation closely matches that of for disease systems with only one type of individual and R-0 where infection is directly transmitted. The parameters defining each matrix element have a clear biological meaning. We illustrate the usefulness and power of the approach with a detailed examination of tick-borne diseases, and we use field and experimental data to parameterize the next-generation matrix for Lyme disease and tick-borne encephalitis. Sensitivity and elasticity analyses of the matrices, at the element and individual parameter levels, allow direct comparison of the two etiological agents. This provides further support that transmission between cofeeding ticks is critically important for the establishment of tick-borne encephalitis.