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谁是感染者?有症状和无症状病例的传染病模型。

Who is the infector? Epidemic models with symptomatic and asymptomatic cases.

机构信息

Department of mathematics, Stockholm University, Stockholm 106 91, Sweden.

出版信息

Math Biosci. 2018 Jul;301:190-198. doi: 10.1016/j.mbs.2018.04.002. Epub 2018 Apr 11.

Abstract

What role do asymptomatically infected individuals play in the transmission dynamics? There are many diseases, such as norovirus and influenza, where some infected hosts show symptoms of the disease while others are asymptomatically infected, i.e. do not show any symptoms. The current paper considers a class of epidemic models following an SEIR (Susceptible  →  Exposed  →  Infectious  →  Recovered) structure that allows for both symptomatic and asymptomatic cases. The following question is addressed: what fraction ρ of those individuals getting infected are infected by symptomatic (asymptomatic) cases? This is a more complicated question than the related question for the beginning of the epidemic: what fraction of the expected number of secondary cases of a typical newly infected individual, i.e. what fraction of the basic reproduction number R, is caused by symptomatic individuals? The latter fraction only depends on the type-specific reproduction numbers, while the former fraction ρ also depends on timing and hence on the probabilistic distributions of latent and infectious periods of the two types (not only their means). Bounds on ρ are derived for the situation where these distributions (and even their means) are unknown. Special attention is given to the class of Markov models and the class of continuous-time Reed-Frost models as two classes of distribution functions for latent and infectious periods. We show how these two classes of models can exhibit very different behaviour.

摘要

无症状感染者在传播动力学中扮演什么角色?有许多疾病,如诺如病毒和流感,其中一些受感染的宿主表现出疾病的症状,而另一些则无症状感染,即没有任何症状。本文考虑了一类遵循 SEIR(易感者→暴露者→感染者→康复者)结构的传染病模型,该模型允许同时存在有症状和无症状病例。以下问题被提出:有多少比例 ρ 的感染者是由有症状(无症状)病例感染的?这是一个比传染病早期更复杂的问题:一个典型新感染者预期的继发性病例数的多少比例,即基本再生数 R 的多少比例,是由有症状的个体引起的?后者仅取决于特定类型的繁殖数,而前者 ρ 还取决于时间,因此取决于两种类型的潜伏期和传染期的概率分布(不仅仅是它们的平均值)。对于这些分布(甚至其平均值)未知的情况,推导出了 ρ 的界限。特别关注马尔可夫模型类和连续时间里德-弗罗斯特模型类,作为潜伏期和传染期的两类分布函数。我们展示了这两类模型如何表现出非常不同的行为。

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