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相关论文: A Degree Based Approximation of an SIR Model with …

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The SIR model is used extensively in the field of epidemiology, in particular, for the analysis of communal diseases. One problem with SIR and other existing models is that they are tailored to random or Erdos type networks since they do…

社会与信息网络 · 计算机科学 2014-10-22 M. S. S. Khan

We study the classic Susceptible-Infected-Recovered (SIR) model for the spread of an infectious disease. In this stochastic process, there are two competing mechanism: infection and recovery. Susceptible individuals may contract the disease…

种群与进化 · 定量生物学 2012-05-08 E. Ben-Naim , P. L. Krapivsky

The duration, type and structure of connections between individuals in real-world populations play a crucial role in how diseases invade and spread. Here, we incorporate the aforementioned heterogeneities into a model by considering a…

物理与社会 · 物理学 2018-04-05 Rosanna C Barnard , Istvan Z Kiss , Luc Berthouze , Joel C Miller

In the recent COVID-19 pandemic we assisted at a sequence of epidemic waves intertwined by anomalous fade-outs with periods of low but persistent epidemic prevalence. These long-living epidemic states complicate epidemic control and…

物理与社会 · 物理学 2025-08-27 Javier Aguilar , Beatriz Arregui García , Raúl Toral , Sandro Meloni , Jose J. Ramasco

We study the phase transition from the persistence phase to the extinction phase for the SIRS (susceptible/ infected/ refractory/ susceptible) model of diseases spreading on the networks. We derive an analytical expression of the…

种群与进化 · 定量生物学 2019-11-18 M. Ali Saif

In this work, we study the epidemic SIR model on a system which takes into consideration face-to-face interaction networks. This approach has been used as prototype to describe people interactions in different kinds of social organizations…

We study the susceptible-infected-susceptible (SIS) model on directed complex networks within the quenched mean-field approximation. Combining results from random matrix theory with an analytic approach to the distribution of fixed-point…

物理与社会 · 物理学 2025-12-12 Vinícius B. Müller , Fernando L. Metz

A probabilistic approach to the epidemic evolution on realistic social-contact networks allows for characteristic differences among subjects, including the individual number and structure of social contacts, and the heterogeneity of the…

社会与信息网络 · 计算机科学 2022-02-11 Jan B. Broekaert , Davide La Torre , Faizal Hafiz

This paper proposes a novel discrete-time multi-virus susceptible-infected-recovered (SIR) model that captures the spread of competing epidemics over a population network. First, we provide sufficient conditions for the infection level of…

系统与控制 · 电气工程与系统科学 2023-05-17 Ciyuan Zhang , Sebin Gracy , Tamer Basar , Philip E. Pare

In this paper we present a model describing Susceptible-Infected-Susceptible (SIS) type epidemics spreading on a dynamic contact network with random link activation and deletion where link ac- tivation can be locally constrained. We use and…

动力系统 · 数学 2015-05-30 Michael Taylor , Timothy J. Taylor , Istvan Z. Kiss

Studies on how to model the interplay between diseases and behavioral responses (so-called coupled disease-behavior interaction) have attracted increasing attention. Owing to the lack of obvious clinical evidence of diseases, or the…

物理与社会 · 物理学 2016-08-24 Hai-Feng Zhang , Jia-Rong Xie , Han-Shuang Chen , Can Liu , Michael Small

Exact solution of the Susceptible-Infectious-Recovered (SIR) epidemic model is derived, and various properties of solution are obtained directly from the exact solution. It is shown that there exists an exact solution of an initial value…

种群与进化 · 定量生物学 2022-10-04 Norio Yoshida

The integration of empirical data in computational frameworks to model the spread of infectious diseases poses challenges that are becoming pressing with the increasing availability of high-resolution information on human mobility and…

种群与进化 · 定量生物学 2013-04-24 Anna Machens , Francesco Gesualdo , Caterina Rizzo , Alberto E Tozzi , Alain Barrat , Ciro Cattuto

We study the susceptible-infective-recovered (SIR) epidemic on a random graph chosen uniformly subject to having given vertex degrees. In this model infective vertices infect each of their susceptible neighbours, and recover, at a constant…

概率论 · 数学 2014-09-24 Svante Janson , Malwina Luczak , Peter Windridge

The SIR model is a three-compartment model of the time development of an epidemic. After normalizing the dependent variables, the model is a system of two non-linear differential equations for the susceptible proportion $S$ and the infected…

动力系统 · 数学 2021-04-27 William G. Faris

There is increasing evidence that one of the most difficult problems in trying to control the ongoing COVID-19 epidemic is the presence of a large cohort of asymptomatic infectives. We develop a SIR-type model taking into account the…

种群与进化 · 定量生物学 2020-06-29 Giuseppe Gaeta

Many complex networks exhibit vulnerability to spreading of epidemics, and such vulnerability relates to the viral strain as well as to the network characteristics. For instance, the structure of the network plays an important role in…

物理与社会 · 物理学 2015-03-14 Mina Youssef , Caterina Scoglio

This paper investigates a behavioral-feedback SIR model in which the infection rate adapts dynamically based on the fractions of susceptible and infected individuals. We introduce an invariant of motion and we characterize the peak of…

种群与进化 · 定量生物学 2025-09-17 Martina Alutto , Leonardo Cianfanelli , Giacomo Como , Fabio Fagnani , Francesca Parise

The Susceptible-Infected-Recovered (SIR) model is the cornerstone of epidemiological models. However, this specification depends on two parameters only, which implies a lack of flexibility and the difficulty to replicate the volatile…

种群与进化 · 定量生物学 2020-11-17 Christian Gourieroux , Yang Lu

We show that precise knowledge of epidemic transmission parameters is not required to build an informative model of the spread of disease. We propose a detailed model of the topology of the contact network under various external control…

种群与进化 · 定量生物学 2020-06-15 Michael Small , David Cavanagh