Ball Frank, Pellis Lorenzo, Trapman Pieter
School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
Warwick Infectious Disease Epidemiology Research Centre (WIDER) and Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.
Math Biosci. 2016 Apr;274:108-39. doi: 10.1016/j.mbs.2016.01.006. Epub 2016 Feb 2.
In this paper we consider epidemic models of directly transmissible SIR (susceptible → infective → recovered) and SEIR (with an additional latent class) infections in fully-susceptible populations with a social structure, consisting either of households or of households and workplaces. We review most reproduction numbers defined in the literature for these models, including the basic reproduction number R0 introduced in the companion paper of this, for which we provide a simpler, more elegant derivation. Extending previous work, we provide a complete overview of the inequalities among these reproduction numbers and resolve some open questions. Special focus is put on the exponential-growth-associated reproduction number Rr, which is loosely defined as the estimate of R0 based on the observed exponential growth of an emerging epidemic obtained when the social structure is ignored. We show that for the vast majority of the models considered in the literature Rr ≥ R0 when R0 ≥ 1 and Rr ≤ R0 when R0 ≤ 1. We show that, in contrast to models without social structure, vaccination of a fraction 1-1/R0 of the population, chosen uniformly at random, with a perfect vaccine is usually insufficient to prevent large epidemics. In addition, we provide significantly sharper bounds than the existing ones for bracketing the critical vaccination coverage between two analytically tractable quantities, which we illustrate by means of extensive numerical examples.
在本文中,我们考虑在具有社会结构(由家庭或家庭与工作场所组成)的完全易感人群中,直接传播的SIR(易感→感染→康复)和SEIR(带有一个额外的潜伏类)传染病的流行模型。我们回顾了文献中为这些模型定义的大多数繁殖数,包括在本文的配套论文中引入的基本繁殖数(R_0),对此我们给出了一个更简单、更优美的推导。扩展先前的工作,我们全面概述了这些繁殖数之间的不等式,并解决了一些悬而未决的问题。特别关注与指数增长相关的繁殖数(R_r),它大致定义为在忽略社会结构时,基于新出现疫情的观察到的指数增长对(R_0)的估计。我们表明,对于文献中考虑的绝大多数模型,当(R_0\geq1)时(R_r\geq R_0),当(R_0\leq1)时(R_r\leq R_0)。我们表明,与没有社会结构的模型不同,用完美疫苗对随机均匀选择的(1 - 1/R_0)比例的人群进行疫苗接种通常不足以预防大规模疫情。此外,我们给出了比现有界限明显更精确的界限,用于将关键疫苗接种覆盖率界定在两个易于分析处理的量之间,我们通过大量数值示例对此进行了说明。