Phenomenological Models of Three Scenarios of Local SARS-COV-2 Coronavirus Epidemics in New York, Brazil, and Japan

Q3 Mathematics
A. Yu. Perevaryukha
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Abstract

The COVID-19 pandemic did not end in the summer of 2023 but moved into the stage of a dynamic confrontation between a mutating pathogen and herd immunity (natural and vaccine). Pandemic influenza strains were guaranteed to die out after three waves. SARS-COV-2 is able to maintain variability in its E and S proteins. The diversity of SARS-COV-2 strains increases in bursts (XBB.x in India, XBC in the Philippines). Most strains drop out of distribution, but the remaining ones give rise to new branches such as BA.2.86 Pirola and its descendants of the JN.x series, active in the winter wave of 2024. The evolution is reflected by the pulsation in the number of recorded infections, but the frequency and amplitude of the peaks differ in regions. Regional epidemic scenarios are emerging, and some of them are unusual. It is not only the property of the variability of the antigens of the virus that leads to new repeated outbreaks after the attenuation of the oscillations in the number of infections. Regional epidemic scenarios are being formed, some of them are unusual and interesting for modeling. For a phenomenological model description of scenarios for the emergence of new waves, we propose equations with a delay as a flexible tool for analyzing complex forms of oscillatory dynamics. The equations are supplemented with special threshold damping functions. In the models, it is possible to obtain scenarios of both collapsing and damping oscillations with the possibility of a new outbreak, which describes the effect of a single extreme wave after an increase in the length of active infection chains in New York with a sharp J-shaped peak with oscillatory attenuation that stands out sharply among the morbidity oscillations. The wave scenario in Brazil differs significantly from both the primary outbreak in 2020 and the specific epidemic scenario in Japan in 2022–2023 in the form of a series of eight consecutive short peaks with increasing wave amplitude. Since the coronavirus successfully counteracts the immune system, there is an increase in severe cases of reinfection with COVID-19 in a group that is particularly susceptible. An important factor for slowing down the evolution of the virus is heterogeneity of the population immunity, when activated T-lymphocytes and antibodies produced in the population are able to respond to a wide range of epitopes from different conservative regions of proteins. In the spring of 2024 a new strain JN with significantly reduced affinity for the cell receptor spreads.

Abstract Image

纽约、巴西和日本地方性 SARS-COV-2 冠状病毒流行三种情况的现象学模型
摘要 COVID-19 大流行并没有在 2023 年夏天结束,而是进入了变异病原体与群体免疫(天然免疫和疫苗免疫)之间的动态对抗阶段。流感大流行菌株在经历三波后必将消亡。SARS-COV-2 能够保持其 E 蛋白和 S 蛋白的变异性。SARS-COV-2 株系的多样性在爆发时会增加(印度的 XBB.x,菲律宾的 XBC)。大多数菌株退出了分布,但剩下的菌株又产生了新的分支,如 BA.2.86 Pirola 及其 JN.x 系列的后代,在 2024 年的冬季浪潮中十分活跃。记录在案的感染数量的脉动反映了这一演变,但不同地区的峰值频率和振幅各不相同。区域性流行病情况正在出现,其中一些是不寻常的。病毒抗原的变异性并不仅仅是在感染数量的振荡减弱后导致新的重复爆发的特性。目前正在形成区域性疫情,其中一些疫情不同寻常,对建模很有意义。为了从现象学角度描述新一波疫情出现的情景,我们提出了带延迟的方程,作为分析复杂形式振荡动力学的灵活工具。这些方程辅以特殊的阈值阻尼函数。在这些模型中,我们可以得到有可能爆发新疫情的坍塌振荡和阻尼振荡情景,这描述了在纽约活动感染链长度增加后出现的单次极端波浪的影响,该波浪在发病率振荡中突出地表现为具有振荡衰减的尖锐 J 形峰值。巴西的波浪情景与 2020 年的原发疫情和 2022-2023 年日本的特定疫情情景明显不同,其形式为一系列连续的 8 个短峰值,波幅不断增大。由于冠状病毒能成功抵消免疫系统的作用,COVID-19 的重症再感染病例在特别易感人群中有所增加。减缓病毒进化的一个重要因素是群体免疫力的异质性,即群体中产生的活化 T 淋巴细胞和抗体能够对蛋白质不同保守区的多种表位做出反应。2024 年春天,一种对细胞受体亲和力明显降低的新毒株 JN 扩散开来。
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来源期刊
Mathematical Models and Computer Simulations
Mathematical Models and Computer Simulations Mathematics-Computational Mathematics
CiteScore
1.20
自引率
0.00%
发文量
99
期刊介绍: Mathematical Models and Computer Simulations  is a journal that publishes high-quality and original articles at the forefront of development of mathematical models, numerical methods, computer-assisted studies in science and engineering with the potential for impact across the sciences, and construction of massively parallel codes for supercomputers. The problem-oriented papers are devoted to various problems including industrial mathematics, numerical simulation in multiscale and multiphysics, materials science, chemistry, economics, social, and life sciences.
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