SEIR modeling of the COVID-19 and its dynamics

He, Shaobo; Peng, Yuexi; Sun, Kehui

doi:10.1007/s11071-020-05743-y

Cited by 598 publications

(490 citation statements)

References 35 publications

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“…As the epidemic spreads, researchers realized that these models were unable to accurately predict the real evolution of the pandemic, even in individual countries or small regions. As a consequence, most researchers directed their effort towards developing more fitting models, for instance, adding more compartments to the model such as pre-symptomatic, hospitalized, requiring intense care units, quarantined, isolated and exposed, isolated and infected, recovered or dead, see References [11][12][13][14], possibly leading to over-parameterized models. Other studies propose age-structured models that divide the population in classes depending on their age [15,16] or coupling them with available mobility data to study the effect of people's mobility in the spread of the disease [17,18].…”

Section: Related Workmentioning

confidence: 99%

A Spatial-Temporal Model for the Evolution of the COVID-19 Pandemic in Spain Including Mobility

et al. 2020

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In this work, a model for the simulation of infectious disease outbreaks including mobility data is presented. The model is based on the SAIR compartmental model and includes mobility data terms that model the flow of people between different regions. The aim of the model is to analyze the influence of mobility on the evolution of a disease after a lockdown period and to study the appearance of small epidemic outbreaks due to the so-called imported cases. We apply the model to the simulation of the COVID-19 in the various areas of Spain, for which the authorities made available mobility data based on the position of cell phones. We also introduce a method for the estimation of incomplete mobility data. Some numerical experiments show the importance of data completion and indicate that the model is able to qualitatively simulate the spread tendencies of small outbreaks. This work was motivated by an open call made to the mathematical community in Spain to help predict the spread of the epidemic.

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Section: Related Workmentioning

confidence: 99%

A Spatial-Temporal Model for the Evolution of the COVID-19 Pandemic in Spain Including Mobility

et al. 2020

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“…In addition, a numerical comparison between our two-strain epidemic model results and COVID-19 clinical data will be conducted. It will be worthy to notice that the dynamics of SEIR COVID-19 epidemic model with two bilinear incidence functions was tackled in [35], and the authors introduce seasonality and stochasticity in order to describe the infection rate parameters. Taking into account non-monotone incidence function, the technique of sliding mode control was used to study an SEIR epidemic model describing COVID-19 disease [36].…”

Section: Introductionmentioning

confidence: 99%

Global dynamics of a multi-strain SEIR epidemic model with general incidence rates: application to COVID-19 pandemic

2020

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This paper investigates the global stability analysis of two-strain epidemic model with two general incidence rates. The problem is modelled by a system of six nonlinear ordinary differential equations describing the evolution of susceptible, exposed, infected and removed individuals. The wellposedness of the suggested model is established in terms of existence, positivity and boundedness of solutions. Four equilibrium points are given, namely the disease-free equilibrium, the endemic equilibrium with respect to strain 1, the endemic equilibrium with respect to strain 2, and the last endemic equilibrium with respect to both strains. By constructing suitable Lyapunov functional, the global stability of the disease-free equilibrium is proved depending on the basic reproduction number R 0. Furthermore, using other appropriate Lyapunov functionals, the global stability results of the endemic equilibria are established depending on the strain 1 reproduction number R 1 0 and the strain 2 reproduction number R 2 0. Numerical simulations are performed in order to confirm the different theoretical results. It was observed that the model with a generalized incidence functions encompasses a large number of models with classical incidence functions and it gives a significant wide view about the equilibria stability. Numerical comparison between the model results and COVID-19

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“…Data from Italy, the United Kingdom, and the United States were shown to fit well the model. A wealth of di↵erent compartmental models were recently proposed to understand the influence of asymptomatic individuals and the e↵ects of control measures on the evolution of the disease [15], SEIR models combined with particle swarm optimization algorithm for parameter optimization [16,17], a SAIR model in the context of social networks [18], a SEIRD model with classical and fractional-order derivatives based on data in Italy to show that the fractional-order model has less RMS error than the classical one [19].…”

Section: Introductionmentioning

confidence: 99%

Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy

Quaranta

Formica

Machado

et al. 2020

Preprint

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The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of an SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilaritiesof the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way towards a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

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SEIR modeling of the COVID-19 and its dynamics

Cited by 598 publications

References 35 publications

A Spatial-Temporal Model for the Evolution of the COVID-19 Pandemic in Spain Including Mobility

A Spatial-Temporal Model for the Evolution of the COVID-19 Pandemic in Spain Including Mobility

Global dynamics of a multi-strain SEIR epidemic model with general incidence rates: application to COVID-19 pandemic

Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy

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