Calculation of disease dynamics in a population of households.
Calculation of disease dynamics in a population of households.
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DOI：
10.1371/journal.pone.0009666
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发表时间：
2010-03-18
期刊：
影响因子：
3.7
通讯作者：
Keeling MJ
中科院分区：
文献类型：
--
作者：
Ross JV;House T;Keeling MJ
传染病动力学的早期数学表示假设有一个单一的、大量的、均匀混合的群体。在过去的十年中，人们对由多个较小的亚群（家庭、工作场所、学校、社区）组成的模型越来越感兴趣，自然假设每个亚群内具有很强的同质混合性，而亚群之间的传播较弱。在这里，我们考虑一个非常大（假设无限）的家庭人口中的 SIRS（易感-感染-恢复-易感）感染动态模型，并简化假设每个家庭的规模相同（尽管所有方法都可以扩展到家庭规模分布异质的人口）。对于这个家庭模型，我们提出了有效的方法来研究流行病学感兴趣的几个数量：（i）入侵阈值； (ii) 早期增长率； (iii) 家庭子女分布； (iv) 感染的流行情况； (v) 过程的瞬态动力学。我们利用这些方法来探索适合人类传染病的广泛参数空间区域。然后，我们扩展这些结果以考虑更现实的伽马分布感染期的影响。我们讨论所有这些结果与标准均质混合模型的不同之处，并评估对感染的入侵、传播和持续性的影响。这里提出的方法的计算效率有望有助于结构化模型的参数化以及评估未来疾病爆发的适当反应。
Early mathematical representations of infectious disease dynamics assumed a single, large, homogeneously mixing population. Over the past decade there has been growing interest in models consisting of multiple smaller subpopulations (households, workplaces, schools, communities), with the natural assumption of strong homogeneous mixing within each subpopulation, and weaker transmission between subpopulations. Here we consider a model of SIRS (susceptible-infectious-recovered-susceptible) infection dynamics in a very large (assumed infinite) population of households, with the simplifying assumption that each household is of the same size (although all methods may be extended to a population with a heterogeneous distribution of household sizes). For this households model we present efficient methods for studying several quantities of epidemiological interest: (i) the threshold for invasion; (ii) the early growth rate; (iii) the household offspring distribution; (iv) the endemic prevalence of infection; and (v) the transient dynamics of the process. We utilize these methods to explore a wide region of parameter space appropriate for human infectious diseases. We then extend these results to consider the effects of more realistic gamma-distributed infectious periods. We discuss how all these results differ from standard homogeneous-mixing models and assess the implications for the invasion, transmission and persistence of infection. The computational efficiency of the methodology presented here will hopefully aid in the parameterisation of structured models and in the evaluation of appropriate responses for future disease outbreaks.