Gökaydin Dinis, Oliveira-Martins José B, Gordo Isabel, Gomes M Gabriela M
Instituto Gulbenkian de Ciência, Apartado 14, 2781-901 Oeiras, Portugal.
J R Soc Interface. 2007 Feb 22;4(12):137-42. doi: 10.1098/rsif.2006.0159.
The awareness that pathogens can adapt and evolve over relatively short time-scales is changing our view of infectious disease epidemiology and control. Research on the transmission dynamics of antigenically diverse pathogens is progressing and there is increasing recognition for the need of new concepts and theories. Mathematical models have been developed considering the modelling unit in two extreme scales: either diversity is not explicitly represented or diversity is represented at the finest scale of single variants. Here, we use an intermediate approach and construct a model at the scale of clusters of variants. The model captures essential properties of more detailed systems and is much more amenable to mathematical treatment. Specificities of pathogen clusters and the overall potential for transmission determine the reinfection rates. These are, in turn, important regulators of cluster dynamics. Ultimately, we detect a reinfection threshold (RT) that separates different behaviours along the transmissibility axis: below RT, levels of infection are low and cluster substitutions are probable; while above RT, levels of infection are high and multiple cluster coexistence is the most probable outcome.
病原体能够在相对较短的时间尺度上适应和进化,这一认识正在改变我们对传染病流行病学和控制的看法。关于抗原性多样的病原体传播动力学的研究正在取得进展,人们越来越认识到需要新的概念和理论。已经开发了数学模型,这些模型考虑了两个极端尺度上的建模单元:要么不明确表示多样性,要么在单个变体的最精细尺度上表示多样性。在这里,我们采用一种中间方法,在变体簇的尺度上构建一个模型。该模型捕捉了更详细系统的基本属性,并且更易于进行数学处理。病原体簇的特异性和总体传播潜力决定了再感染率。反过来,这些又是簇动态的重要调节因素。最终,我们检测到一个再感染阈值(RT),它沿着传播轴将不同的行为区分开来:低于RT时,感染水平较低,簇替代是可能的;而高于RT时,感染水平较高,多个簇共存是最可能的结果。