A new SAIR model on complex networks for analysing the 2019 novel coronavirus (COVID-19)

Abstract

Nowadays, the novel coronavirus (COVID-19) is spreading around the world and has attracted extremely wide public attention. From the beginning of the outbreak to now, there have been many mathematical models proposed to describe the spread of the pandemic, and most of them are established with the assumption that people contact with each other in a homogeneous pattern. However, owing to the difference of individuals in reality, social contact is usually heterogeneous, and the models on homogeneous networks cannot accurately describe the outbreak. Thus, we propose a susceptible-asymptomaticinfected-removed (SAIR) model on social networks to describe the spread of COVID-19 and analyse the outbreak based on the epidemic data of Wuhan from January 24 to March 2. Then, according to the results of the simulations, we discover that the measures that can curb the spread of COVID-19 include increasing the

Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control

“…Nowadays, the dynamics of COVID-19 models have been growing interest in the research community and may mathematical models are designed for the better interest of people around the world, such as the model of eight classes based on susceptible, infected, diagnosed, ailing, recognized, threatened, healed and extinct (SIDARTHE) [6], five classes based on SEIAR represented with 5 number of ordinary differential equations [7], a new θ -SEIHRD model represented with nine classes [8], modified SEIRS model system with five classes [9], four class modified SIR model [10], SAIR system based COVID-19 model for complex networks [11]. Beside, these variety of COVID-19 model are introduced by the researchers [8,[12][13][14][15][16][17][18][19][20][21][22].…”

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

“…Our model is based on the less common compartmental model called SAIR (Suspected-Asymptomatic-Infected-Retired) model [19], which substitutes the E compartment of the SEIR model by the A compartment, modelling the infectious asymptomatic individuals. This model is relevant when there are many undetected asymptomatic infectious individuals in the A compartment and symptomatic detected infectious individuals in the I compartment and has been used to model diseases with a very high degree of asymptomatic individuals, as for example dengue [20] and of course COVID-19 [21,22].…”

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

“…Data from Italy, the UK, and the USA were shown to fit well the model. A wealth of different compartmental models were recently proposed to understand the influence of asymptomatic individuals and the effects of control measures on the evolution of the disease [ 18 ], SEIR models combined with particle swarm optimization algorithm for parameter optimization [ 19 , 20 ], a SAIR model in the context of social networks [ 21 ], 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 [ 22 ].…”