新型冠状病毒肺炎无症状感染者及再感染影响的数学模型

Arpita Devi, Asish Adak, Praveen Kumar Gupta
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引用次数: 0

摘要

在本研究中,考虑了具有恒定易感个体招募率和双线性传染率的流行病模型。对疫苗型治疗进行检查,以尽量减少疾病的影响。根据2020-2025年新型冠状病毒病(COVID-19)大流行的情况,选取无症状感染者和接种疫苗的隔间。这两个隔间的影响被专门研究,以阐明行为动力学。通过构造Lyapunov函数检验了无病平衡点和地方性平衡点的局部稳定性和全局稳定性。因此,我们证明,如果基本繁殖数< 1,则系统中不存在疾病,如果基本繁殖数为> 1,则该疾病将持续存在。进行敏感性分析,以确定对所建议模型的原始再现数影响最大的有影响的模型参数。通过与实际数据的拟合,验证了模型的正确性。此外,我们还进行了模型参数的数值模拟和相应的理论结果。为了控制或消除新发疾病的影响,我们提出了几点建议,在采取必要的预防措施的同时控制最敏感的模型参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Mathematical Model Evaluating the Impact of Asymptomatic COVID-19 Cases and Reinfection.

In this study, an epidemic model with a constant recruitment rate of susceptible individuals with a bilinear transmission rate of infection is considered. Vaccination-type treatment is inspected to minimize the impact of the disease. The asymptomatic infected and the vaccinated compartments are taken with regard to the circumstances of the coronavirus disease (COVID-19) pandemic 2020-2025. The impact of these two compartments is examined specifically to shed light on the behavioral dynamics. Local as well as the global stability of the disease-free equilibrium point and the endemic equilibrium point are examined by constructing Lyapunov functions. Hence, we prove that if the basic reproduction number is < 1, then there will be no disease in the system, and if the basic reproduction number is > 1, then the disease will persist. Sensitivity analysis is performed to identify the influential model parameters that have the greatest impact on the original reproduction number of the proposed model. The model is validated by fitting it to real data. Furthermore, we carried out numerical simulations of the model parameters and their accompanying theoretical results. To control or eliminate the effect of emerging diseases, we made several suggestions to control the most sensitive model parameters while using necessary preventive measures.

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