Seasonally Forced SIR Systems Applied to Respiratory Infectious Diseases, Bifurcations, and Chaos

IF 0.9 Q3 MATHEMATICS, APPLIED Computational and Mathematical Methods Pub Date : 2022-03-03 DOI:10.1155/2022/3556043
Nico Stollenwerk, Stefano Spaziani, Javier Mar, Irati Eguiguren Arrizabalaga, Damián Knopoff, Nicole Cusimano, Vizda Anam, Akhil Shrivastava, Maíra Aguiar
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Abstract

Summary. We investigate models to describe respiratory diseases with fast mutating virus pathogens such that after some years the aquired resistance is lost and hosts can be infected with new variants of the pathogen. Such models were initially suggested for respiartory diseases like influenza, showing complex dynamics in reasonable parameter regions when comparing to historic empirical influenza like illness data, e.g., from Ille de France. The seasonal forcing typical for respiratory diseases gives rise to the different rich dynamical scenarios with even small parameter changes. Especially the seasonality of the infection leads for small values already to period doubling bifurcations into chaos, besides additional coexisting attractors. Such models could in the future also play a role in understanding the presently experienced COVID-19 pandemic, under emerging new variants and with only limited vaccine efficacies against newly upcoming variants. From first period doubling bifurcations, we can eventually infer at which close by parameter regions complex dynamics including deterministic chaos can arise.

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季节性强迫SIR系统应用于呼吸道传染病、分岔和混沌
总结。我们研究了一些模型来描述具有快速变异病毒病原体的呼吸道疾病,这些病原体在若干年后丧失了获得的抵抗力,宿主可以感染病原体的新变体。这种模型最初是针对流感等呼吸道疾病提出的,与历史上的流感等疾病的经验数据(例如来自Ille de France)相比,在合理的参数区域显示出复杂的动态。典型的呼吸系统疾病的季节强迫产生了不同的丰富的动力情景,甚至参数变化很小。特别是感染的季节性导致小值已经周期加倍分岔混乱,除了额外的共存吸引。这样的模型在未来也可以在理解目前正在经历的COVID-19大流行中发挥作用,在新出现的新变体下,疫苗对即将到来的新变体的效力有限。从第一周期加倍分岔中,我们最终可以推断出在哪个参数区域附近会产生包括确定性混沌在内的复杂动力学。
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