Zhang Xu-Sheng
Department of Statistics, Modelling and Economics, Centre for Infectious Disease Surveillance and Control, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College School of Public Health, Norfolk Place, London, W2 1PG, UK.
Theor Biol Med Model. 2016 Apr 18;13:14. doi: 10.1186/s12976-016-0040-7.
One common observation in infectious diseases caused by multi-strain pathogens is that both the incidence of all infections and the relative fraction of infection with each strain oscillate with time (i.e., so-called Epidemic cycling). Many different mechanisms have been proposed for the pervasive nature of epidemic cycling. Nevertheless, the two facts that people contact each other through a network rather than following a simple mass-action law and most infectious diseases involve multiple strains have not been considered together for their influence on the epidemic cycling.
To demonstrate how the structural contacts among people influences the dynamical patterns of multi-strain pathogens, we investigate a two strain epidemic model in a network where every individual randomly contacts with a fixed number of other individuals. The standard pair approximation is applied to describe the changing numbers of individuals in different infection states and contact pairs.
We show that spatial correlation due to contact network and interactions between strains through both ecological interference and immune response interact to generate epidemic cycling. Compared to one strain epidemic model, the two strain model presented here can generate epidemic cycling within a much wider parameter range that covers many infectious diseases.
Our results suggest that co-circulation of multiple strains within a contact network provides an explanation for epidemic cycling.
在由多菌株病原体引起的传染病中,一个常见的观察结果是,所有感染的发生率以及每种菌株感染的相对比例都会随时间波动(即所谓的流行周期)。针对流行周期的普遍性,人们提出了许多不同的机制。然而,人们通过网络相互接触而非遵循简单的质量作用定律这一事实,以及大多数传染病涉及多种菌株这一事实,尚未被综合考虑它们对流行周期的影响。
为了证明人与人之间的结构接触如何影响多菌株病原体的动态模式,我们在一个网络中研究了一个双菌株流行模型,其中每个个体随机与固定数量的其他个体接触。应用标准对近似来描述处于不同感染状态的个体数量和接触对的变化。
我们表明,由于接触网络导致的空间相关性以及通过生态干扰和免疫反应产生的菌株间相互作用,共同作用产生了流行周期。与单菌株流行模型相比,这里提出的双菌株模型能够在更广泛的参数范围内产生流行周期,涵盖了许多传染病。
我们的结果表明,接触网络中多种菌株的共同传播为流行周期提供了一种解释。