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<p>The purposeful development of special modeling methods for the specific modern epidemic situation with the accumulation of collective immunity, but the preservation of the mutating pathogen in the population, has continued. The evolution of SAR-CoV-2 does not stop due to the problem of chronic disease “Long COVID.” The change in the number of SAR-CoV-2 infections continues to develop in the form of long-term attenuating local waves with significant regional specificity. SIRS models have become ineffective for predicting sudden changes in trends and the shape of COVID-19 waves. As the author showed earlier, the waves arose after a brief period of unstable equilibrium, and the dynamics of situations are qualitatively different in isolated countries dominated by different branches of Omicron. The author has classified scenarios and developed equations for changing the phases of the start and decay of a series of waves with threshold states to describe the properties of a series of alternating series of peaks during the epidemic in Japan and New Zealand. The proposed method of forming equations with a choice of damping functions made it possible to empirically describe scenarios of regional waves of infections. After the end of the activity of the BA.2.86/JN branch strains, despite the unabated rate of emergence of new versions of coronavirus, there is no new global wave of morbidity in the winter of 2005. Convergent strains in most countries did not cause serious consequences, although experts reported on the danger of the XEC strain in the fall of 2024. They did not become dominant despite the ability shown by these variants to evade the antibodies developed in the population against the Omicron BA strain. The rate of spread of XEC and XEK strains is insufficient for a global wave, even with their ability to avoid antibodies, as other activity indicators have weakened. The base reproductive number is the resulting indicator of a number of characteristics of a strain; it is not applicable to mass infections. It is the indicators of virion binding to cells that the author suggests using when building models of epidemic development, taking into account evolution. Neural network-based methods already make it possible to predict a key aspect of the ability of cell infection from detected mutations in new strains, the affinity of S&#xa0;protein binding to the target cell receptor ACE2. The development of a modeling method with the inclusion of tracking the affinity index is proposed for situational redefinition of hybrid computing structures according to monitoring of current mutations and estimating the frequency of occurrence of strains. The prediction of an increase in the affinity of the ACE2–RBD S protein complex for strains entails a restructuring of the function of regulating the effectiveness of the counteraction model and a reassessment of the thresholds. The hybrid predicative structure describes the event-related transformations of the properties of epidemic fluctuations associated with the adaptation of coronavirus in a partially immune population based on the selection of appropriate wave activation and damping functions, which simulates the scenario of the evolution of a series of new COVID waves. In June 2025, waves of two strains NB.1.8.1 and XFG are developing synchronously. The current COVID surge in California is driven by a new subvariant XFG nicknamed “Stratus.”</p>

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Hybrid Models of COVID-19 Epidemic Waves with Evaluation of Changes in the Affinity of Spike Protein Binding to the ACE2 Cellular Receptor

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • A. Yu. Perevaryukha

摘要

The purposeful development of special modeling methods for the specific modern epidemic situation with the accumulation of collective immunity, but the preservation of the mutating pathogen in the population, has continued. The evolution of SAR-CoV-2 does not stop due to the problem of chronic disease “Long COVID.” The change in the number of SAR-CoV-2 infections continues to develop in the form of long-term attenuating local waves with significant regional specificity. SIRS models have become ineffective for predicting sudden changes in trends and the shape of COVID-19 waves. As the author showed earlier, the waves arose after a brief period of unstable equilibrium, and the dynamics of situations are qualitatively different in isolated countries dominated by different branches of Omicron. The author has classified scenarios and developed equations for changing the phases of the start and decay of a series of waves with threshold states to describe the properties of a series of alternating series of peaks during the epidemic in Japan and New Zealand. The proposed method of forming equations with a choice of damping functions made it possible to empirically describe scenarios of regional waves of infections. After the end of the activity of the BA.2.86/JN branch strains, despite the unabated rate of emergence of new versions of coronavirus, there is no new global wave of morbidity in the winter of 2005. Convergent strains in most countries did not cause serious consequences, although experts reported on the danger of the XEC strain in the fall of 2024. They did not become dominant despite the ability shown by these variants to evade the antibodies developed in the population against the Omicron BA strain. The rate of spread of XEC and XEK strains is insufficient for a global wave, even with their ability to avoid antibodies, as other activity indicators have weakened. The base reproductive number is the resulting indicator of a number of characteristics of a strain; it is not applicable to mass infections. It is the indicators of virion binding to cells that the author suggests using when building models of epidemic development, taking into account evolution. Neural network-based methods already make it possible to predict a key aspect of the ability of cell infection from detected mutations in new strains, the affinity of S protein binding to the target cell receptor ACE2. The development of a modeling method with the inclusion of tracking the affinity index is proposed for situational redefinition of hybrid computing structures according to monitoring of current mutations and estimating the frequency of occurrence of strains. The prediction of an increase in the affinity of the ACE2–RBD S protein complex for strains entails a restructuring of the function of regulating the effectiveness of the counteraction model and a reassessment of the thresholds. The hybrid predicative structure describes the event-related transformations of the properties of epidemic fluctuations associated with the adaptation of coronavirus in a partially immune population based on the selection of appropriate wave activation and damping functions, which simulates the scenario of the evolution of a series of new COVID waves. In June 2025, waves of two strains NB.1.8.1 and XFG are developing synchronously. The current COVID surge in California is driven by a new subvariant XFG nicknamed “Stratus.”