Modeling the effect of the vaccination campaign on the COVID-19 pandemic

Angeli, Mattia; Neofotistos, Georgios; Mattheakis, Marios; Kaxiras, Efthimios

doi:10.1016/j.chaos.2021.111621

Cited by 41 publications

(25 citation statements)

References 38 publications

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“…contagious disease has been analytically and numerically studied by using similar epidemiological models (Angeli et al 2022;Grimm et al 2021;Harari and Monteiro 2021;He et al 2020;Ram and Schaposnik 2021).…”

Section: Analytical Resultsmentioning

confidence: 99%

An Epidemic Model with Pro and Anti-vaccine Groups

Harari

Monteiro

2022

Acta Biotheor

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Here, an epidemiological model considering pro and anti-vaccination groups is proposed and analyzed. In this model, susceptible individuals can migrate between these two groups due to the influence of false and true news about safety and efficacy of vaccines. From this model, written as a set of three ordinary differential equations, analytical expressions for the disease-free steady state, the endemic steady state, and the basic reproduction number are derived. It is analytically shown that low vaccination rate and no influx to the pro-vaccination group have similar impacts on the long-term amount of infected individuals. Numerical simulations are performed with parameter values of the COVID-19 pandemic to illustrate the analytical results. The possible relevance of this work is discussed from a public health perspective.

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Section: Analytical Resultsmentioning

confidence: 99%

An Epidemic Model with Pro and Anti-vaccine Groups

Harari

Monteiro

2022

Acta Biotheor

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“…Most of the compartment models are composed of ordinary differential equations(ODE), in which some limitations exist to simulate the real COVID-19 transmission scenarios by four-dimensional ODE systems. Several approaches (or combinations of these methods) are adopted to overcome the limitations of the ODE system modeling [25] such as: using the partial differential equations(PDE), thus the compartments do not merely depend on time [26,27]; considering random effects, and establishing stochastic differential equations (SDE) systems [28,29]; adding more compartments in the deterministic models to discover the essential variables of the epidemic dynamics [30,31]. In our work, we introduced more compartments to characterize the complex COVID-19 transmission and emphasized the effect of asymptomatic infection due to the high proportion of asymptomatic cases [32].…”

Section: Introductionmentioning

confidence: 99%

Why Controlling the Asymptomatic Infection Is Important: A Modelling Study with Stability and Sensitivity Analysis

Pan

Chen

et al. 2022

Fractal Fract

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The large proportion of asymptomatic patients is the major cause leading to the COVID-19 pandemic which is still a significant threat to the whole world. A six-dimensional ODE system (SEIAQR epidemical model) is established to study the dynamics of COVID-19 spreading considering infection by exposed, infected, and asymptomatic cases. The basic reproduction number derived from the model is more comprehensive including the contribution from the exposed, infected, and asymptomatic patients. For this more complex six-dimensional ODE system, we investigate the global and local stability of disease-free equilibrium, as well as the endemic equilibrium, whereas most studies overlooked asymptomatic infection or some other virus transmission features. In the sensitivity analysis, the parameters related to the asymptomatic play a significant role not only in the basic reproduction number R0. It is also found that the asymptomatic infection greatly affected the endemic equilibrium. Either in completely eradicating the disease or achieving a more realistic goal to reduce the COVID-19 cases in an endemic equilibrium, the importance of controlling the asymptomatic infection should be emphasized. The three-dimensional phase diagrams demonstrate the convergence point of the COVID-19 spreading under different initial conditions. In particular, massive infections will occur as shown in the phase diagram quantitatively in the case R0>1. Moreover, two four-dimensional contour maps of Rt are given varying with different parameters, which can offer better intuitive instructions on the control of the pandemic by adjusting policy-related parameters.

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“…We are more concerned about the quantity of the active unconfirmed asymptomatic cases that powerfully determine the development of COVID-19. Generally speaking, existing models about infectious diseases based on the Susceptible-Infected-Removed (SIR) model insert one or two new compartments used to describe asymptomatic cases [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] . However, these ordinary differential equations (ODEs) models face two pivotal problems: the values for the number of individuals in unobservable compartments and fixed coefficients which are largely neglected by the existing research work.…”

Section: Introductionmentioning

confidence: 99%

“…Given this, by daily reported epidemic data, Angeli et al. used semi-supervised neural networks to estimate the initial individual numbers of unobservable compartments and other coefficients in a Susceptible-Asymptomatic-Infected-Vaccinated-Removed (SAIVR) model [18] . But neural networks are not the optimal estimation method because they usually rely on massive sample data, otherwise, the model will be overfitting [19] .…”

Section: Introductionmentioning

confidence: 99%