Dynamics of classical solutions of a multi-strain diffusive epidemic model with mass-action transmission mechanism.

IF 2.2 4区 数学 Q2 BIOLOGY
Jamal Adetola, Keoni G Castellano, Rachidi B Salako
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引用次数: 0

Abstract

We study a diffusive epidemic model and examine the spatial spreading dynamics of a multi-strain infectious disease. In particular, we address the questions of competitive-exclusion or coexistence of the disease's strains. Our results indicate that if one strain has its local reproduction function spatially homogeneous, which either strictly minimizes or maximizes the basic reproduction numbers, then the phenomenon of competitive-exclusion occurs. However, if all the local reproduction functions are spatially heterogeneous, several strains may coexist. In this case, we provide complete information on the large time behavior of classical solutions for the two-strain model when the diffusion rate is uniform within the population and the ratio of the local transmission rates is constant. Particularly, we prove the existence of two critical superimposed functions that serve as threshold values for the ratio of the transmission rates and that of the recovery rates. Furthermore, when the populations' diffusion rates are small, our result on the asymptotic profiles of coexistence endemic equilibria indicate a spatial segregation of infected populations.

具有质量作用传播机制的多菌株扩散流行病模型经典解动力学。
我们研究了一个扩散流行病模型,并研究了多菌株传染病的空间传播动力学。特别是,我们处理这种疾病毒株的竞争排斥或共存问题。研究结果表明,如果一个菌株的局部繁殖函数空间同质,即基本繁殖数严格最小化或最大化,则会出现竞争排斥现象。然而,如果所有的局部繁殖功能在空间上都是异质的,那么几个菌株可能共存。在这种情况下,我们提供了在种群内扩散速率均匀且局部传播速率比恒定时双应变模型经典解的大时间行为的完整信息。特别地,我们证明了两个临界叠加函数的存在性,它们作为传输速率与恢复速率之比的阈值。此外,当种群的扩散速率较小时,我们在共存地方性平衡的渐近曲线上的结果表明感染种群的空间隔离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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