Barbarossa M V, Röst G
Bolyai Institute, University of Szeged, Aradi vértanúk tere 1, 6720, Szeged, Hungary.
J Math Biol. 2015 Dec;71(6-7):1737-70. doi: 10.1007/s00285-015-0880-5. Epub 2015 Apr 2.
When the body gets infected by a pathogen the immune system develops pathogen-specific immunity. Induced immunity decays in time and years after recovery the host might become susceptible again. Exposure to the pathogen in the environment boosts the immune system thus prolonging the time in which a recovered individual is immune. Such an interplay of within host processes and population dynamics poses significant challenges in rigorous mathematical modeling of immuno-epidemiology. We propose a framework to model SIRS dynamics, monitoring the immune status of individuals and including both waning immunity and immune system boosting. Our model is formulated as a system of two ordinary differential equations (ODEs) coupled with a PDE. After showing existence and uniqueness of a classical solution, we investigate the local and the global asymptotic stability of the unique disease-free stationary solution. Under particular assumptions on the general model, we can recover known examples such as large systems of ODEs for SIRWS dynamics, as well as SIRS with constant delay.
当身体受到病原体感染时,免疫系统会产生针对该病原体的免疫力。诱导免疫会随时间衰减,康复数年之后宿主可能会再次变得易感。在环境中接触病原体可增强免疫系统,从而延长康复个体的免疫时间。宿主内部过程与种群动态之间的这种相互作用,给免疫流行病学的严格数学建模带来了重大挑战。我们提出了一个框架来对SIRS动态进行建模,监测个体的免疫状态,并纳入免疫衰退和免疫系统增强因素。我们的模型被表述为一个由两个常微分方程(ODE)与一个偏微分方程(PDE)耦合而成的系统。在证明了经典解的存在性和唯一性之后,我们研究了唯一无病稳态解的局部和全局渐近稳定性。在对一般模型的特定假设下,我们可以恢复已知的例子,如用于SIRWS动态的大型ODE系统,以及具有恒定延迟的SIRS模型。