Epidemic dynamics of influenza-like diseases spreading in complex networks

Abstract

Population contact pattern plays an important role in the spread of an infectious disease. This can be described in the framework of a complex network approach. In this paper network epidemic models for influenza-like diseases that may have infectious force in incubative or asymptomatic stage are formulated and studied. Two general types of network models are considered: the annealed and the quenched networks. The next-generation matrix approach is employed to compute the basic reproduction number of our networkbased models. The implicit equations for the final epidemic size are derived, and the existence and uniqueness of solutions for implicit equations are studied by rewriting implicit equations as suitable fixedpoint problems. In particular, for networks with no degree correlation, low-dimensional systems of nonlinear ordinary differential model are derived by employing an edge-based compartmental approach. Due to their low dimension, a gap between the parameter identification problem for influenza-like diseases or network inference and network epidemic models may be

Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model

“…To protect against COVID-19 epidemic, we must have a deep understanding of the basic characteristics of COVID-19, among which one of the most important features is the incubation period of the virus [ 2 , 14 ]. The incubation period of COVID-19 is the period from infection to the earliest appearance of clinical symptoms of COVID-19 patients [ 15 , 16 ]. Estimation of virus incubation period is of great significance for the epidemiological investigation and the development of epidemic prevention and controlling measures [ 17 ].…”

Impact of information diffusion on epidemic spreading in partially mapping two-layered time-varying networks

“…After more than 20 years of development, complex network theory has made great achievements [15][16][17][18][19], which provides a new framework to favor the study of epidemic transmission. In the epidemic dynamics, the multilayer network structure offers a novel topology foundation to model the epidemic spreading since the spread of epidemic is often coupled with the diffusion of epidemic-related information, such as rumors and knowledges [20][21][22], risk perception [23,24] and fear [25,26], which is usually spread by means of online social networking platforms, (e.g., WeChat, QQ and Twitter) [27,28].…”

On a Discrete SEIR Epidemic Model with Two-Doses Delayed Feedback Vaccination Control on the Susceptible

“…The underlying idea is that in some infectious diseases, such as, for instance, in the COVID-19 pandemic, there are contagions from both exposed to susceptible and from infectious to susceptible. The delays of the vaccination and expected achievable immunity as well as the transmission rates are model parameters which can be updated for different experiments, [ 37 , 38 , 39 ].…”

“…An extended SEIR model which incorporates its usual subpopulations, the asymptomatic, and the dead-infective subpopulations has been proposed in [6]. That vaccination and expected achievable immunity as well as the transmission rates are model parameters which can be updated for different experiments, [37][38][39].…”