Nonlinear dynamics of an epidemic compartment model with asymptomatic infections and mitigation

Görtz, Maurice; Krug, Joachim

doi:10.1088/1751-8121/ac8fc7

Cited by 3 publications

(4 citation statements)

References 37 publications

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“…Also, the dependence of degree heterogeneity (measured by h) on the parameter α cannot be derived explicitly, which is needed to be further investigated. To account for more realistic situation, some mechanisms such as the nonlinear infection [36] should be considered. Besides, with much attention on the dynamical behaviors induced by the synergy and the higher order contagion mechanism [15,[37][38][39][40], including explosive spreading and the emergence of multi-stable region, there are much less efforts attempting to focus on the structural impacts on synergistic spreading.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

From heterogeneous network to homogeneous network: the influence of structure on synergistic epidemic spreading

Lin

Yan

Gao

et al. 2023

J. Phys. A: Math. Theor.

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Synergistic epidemic-like spreading phenomena in networked system occur in various forms in nature and human society. The networks’ structure characterized by its structural heterogeneity aﬀects the synergistic spreading process dramatically. It was believed that the synergistic epidemic spreading follows a continuous transition on heterogeneous networks, but an explosive one on homogeneous networks. In this work, we adopt the model that interpolates between homogeneous and heterogeneous networks to generate a series of studied networks. By continuously changing the ratio of homogeneous structure α of the network, we numerically show that the interplay between the spreading transition and the structural heterogeneity of network is much more complicated. Although the explosive epidemic transition is likely to be hindered by structural heterogeneity, it could occur on completely heterogeneous network as long as the synergistic strength is suﬃciently strong. The predictions of heterogeneous mean-ﬁeld analysis agree with the numerical results, thus helping to understand the role of structural heterogeneity in aﬀecting synergistic epidemic spreading.

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Section: Conclusion and Discussionmentioning

confidence: 99%

From heterogeneous network to homogeneous network: the influence of structure on synergistic epidemic spreading

Lin

Yan

Gao

et al. 2023

J. Phys. A: Math. Theor.

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“…With such a high value of participation ratio (i.e. high level of social participation by members of the infected cohort), coupled with modest general contact propensity, equations (10) and (11) yield R 0 0 = 1.184 and R ∞ 0 = 0.800. Thus R 0 monotonically decreases with ν, becoming less than one at ν = 0.009.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…Vaccination strategies and quality of protection are considered in [15,20,24,25]. Particular role of asymptomatics in epidemics dynamics are the main topic in [1,6,9,11,22]. Leakiness of vaccines is analysed in [17].…”

Section: Introductionmentioning

confidence: 99%

Vaccination, asymptomatics and public health information in COVID-19

Grinfeld,

Mulheran

2024

J. Phys. A: Math. Theor.

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The dynamics of the COVID-19 pandemic are greatly inﬂuenced by vaccine quality, as well as by vaccination rates and the behaviour of infected individuals, both of which reﬂect public policy. We develop a model for the dynamics of relevant cohorts within a ﬁxed population, taking extreme care to model the reduced social contact of infected individuals in a rigorous self-consistent manner. The basic reproduction number R0is then derived in terms of the parameters of the model. Analysis of R0 reveals two interesting possibilities, both of which are plausible based on known characteristics of COVID-19. Firstly, if the population in general moderates social contact, while infected individuals who display clinical symptoms tend not to isolate, then increased vaccination can drive the epidemic towards a disease-free equilibrium (DFE). However, if the reverse is true, then increased vaccination can destabilise the DFE and yield an endemic state. This surprising result is due to the fact that the vaccines are leaky, and can lead to an increase in asymptomatic individuals who unknowingly spread the disease. Therefore, this work shows that public policy regarding the monitoring and release of health information should be combined judiciously with vaccination policy to control COVID-19.

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“…The SIR (susceptible-infected-recovered) model is by far the most commonly used mathematical model based on differential equations [1]. Recently, the SAIR model, or its variants, has been used to address well-known asymptomatic cases [6][7][8][9]. In [10], a SIR model that incorporates awareness and a time delay to account for the latent stage was studied.…”

Section: Introductionmentioning

confidence: 99%

Existence of Traveling Waves of a Diffusive Susceptible–Infected–Symptomatic–Recovered Epidemic Model with Temporal Delay

Miranda,

Arenas,

González-Parra

et al. 2024

Mathematics

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The aim of this article is to investigate the existence of traveling waves of a diffusive model that represents the transmission of a virus in a determined population composed of the following populations: susceptible (S), infected (I), asymptomatic (A), and recovered (R). An analytical study is performed, where the existence of solutions of traveling waves in a bounded domain is demonstrated. We use the upper and lower coupled solutions method to achieve this aim. The existence and local asymptotic stability of the endemic (Ee) and disease-free (E0) equilibrium states are also determined. The constructed model includes a discrete-time delay that is related to the incubation stage of a virus. We find the crucial basic reproduction number R0, which determines the local stability of the steady states. We perform numerical simulations of the model in order to provide additional support to the theoretical results and observe the traveling waves. The model can be used to study the dynamics of SARS-CoV-2 and other viruses where the disease evolution has a similar behavior.

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Nonlinear dynamics of an epidemic compartment model with asymptomatic infections and mitigation

Cited by 3 publications

References 37 publications

From heterogeneous network to homogeneous network: the influence of structure on synergistic epidemic spreading

From heterogeneous network to homogeneous network: the influence of structure on synergistic epidemic spreading

Vaccination, asymptomatics and public health information in COVID-19

Existence of Traveling Waves of a Diffusive Susceptible–Infected–Symptomatic–Recovered Epidemic Model with Temporal Delay

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