Household epidemic models with varying infection response.
Household epidemic models with varying infection response.
复制标题
具有不同感染反应的家庭流行病模型。
DOI：
10.1007/s00285-010-0372-6
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发表时间：
2011
影响因子：
1.9
通讯作者：
Ball F
中科院分区：
文献类型：
--
作者：
Ball F
本文关注的是SIR（易感→感染→移除）家庭流行病模型，其中感染反应可能是轻微的或严重的，反应的类型也会影响个体的传染性。分析了两种不同的模型。在第一个模型中，个体的感染状态是预先确定的，可能是由于部分免疫力，而在第二种模型中，个体的感染状态取决于其感染者的感染状态以及个体是否是通过家庭内部或家庭之间的接触感染的。第一种情况可以使用多类型家庭流行病模型来建模，第二种情况可以使用我们用感染者相关严重性家庭流行病模型表示的模型来建模。得出了两个模型的大人口结果，重点是在流行病爆发的情况下，任何给定规模的典型家庭中轻症和重症病例总数的分布。本文的目的是调查在给定包含轻度和重症病例的最终家庭疫情数据规模时，是否有可能确定两种根本解释中哪一种导致了不同的反应。我们进行的数值研究表明，给定足够多家庭的数据，通常可以通过将两个拟合模型的 Kullback-Leibler 散度与这些数据进行比较来区分这两个模型。
This paper is concerned with SIR (susceptible → infected → removed) household epidemic models in which the infection response may be either mild or severe, with the type of response also affecting the infectiousness of an individual. Two different models are analysed. In the first model, the infection status of an individual is predetermined, perhaps due to partial immunity, and in the second, the infection status of an individual depends on the infection status of its infector and on whether the individual was infected by a within- or between-household contact. The first scenario may be modelled using a multitype household epidemic model, and the second scenario by a model we denote by the infector-dependent-severity household epidemic model. Large population results of the two models are derived, with the focus being on the distribution of the total numbers of mild and severe cases in a typical household, of any given size, in the event that the epidemic becomes established. The aim of the paper is to investigate whether it is possible to determine which of the two underlying explanations is causing the varying response when given final size household outbreak data containing mild and severe cases. We conduct numerical studies which show that, given data on sufficiently many households, it is generally possible to discriminate between the two models by comparing the Kullback–Leibler divergence for the two fitted models to these data.