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Changing environmental conditions can significantly affect the dynamics of disease spread. These changes may arise naturally or result from human interventions; in the latter case, lockdown measures that lead to abrupt but temporary…

统计力学 · 物理学 2026-05-29 Elad Korngut , Michael Assaf

We propose a compartmental model for a disease with temporary immunity and secondary infections. From our assumptions on the parameters involved in the model, the system naturally evolves in three time scales. We characterize the equilibria…

动力系统 · 数学 2024-04-10 Panagiotis Kaklamanos , Andrea Pugliese , Mattia Sensi , Sara Sottile

Starting from a stochastic individual-based description of an SIS epidemic spreading on a random network, we study the dynamics when the size $n$ of the network tends to infinity. We recover in the limit an infinite-dimensional…

In this paper, we analyze the infection spreading dynamics of malware in a population of cyber nodes (i.e., computers or devices). Unlike most prior studies where nodes are reactive to infections, in our setting some nodes are active…

系统与控制 · 电气工程与系统科学 2023-05-25 Keith Paarporn , Shouhuai Xu

The resurgence of vector-borne diseases is an increasing public health concern, and there is a need for a better understanding of their dynamics. For a number of diseases, e.g. dengue and chikungunya, this resurgence occurs mostly in urban…

种群与进化 · 定量生物学 2017-04-14 Abderrahman Iggidr , Gauthier Sallet , Max O. Souza

We study the qualitative properties of a spatial diffusive heterogeneous SIR model, that appears in mathematical epidemiology to describe the spread of an infectious disease in a population. The model we consider consists in a system of…

偏微分方程分析 · 数学 2020-05-15 Romain Ducasse

In this paper, we focus on the node-based epidemic modeling for networks, introduce the propagation medium and propose a node-based Susceptible-Infected-Recovered-Susceptible (SIRS) epidemic model with infective media. Theoretical…

物理与社会 · 物理学 2019-01-03 Leyi Zheng , Longkun Tang

We study a generalization of the classical contact process (SIS epidemic model) in a directed graph $G$. Our model is a continuous-time interacting particle system in which at every time, each vertex is either healthy or infected, and each…

概率论 · 数学 2020-11-26 Shirshendu Chatterjee , David Sivakoff , Matthew Wascher

We propose a new paradigm to design a network-based self-adaptive epidemic model that relies on the interplay between the network and its line graph. We implement this proposal on a Susceptible-Infected-Susceptible model in which both nodes…

适应与自组织系统 · 物理学 2024-03-05 Paolo Bartesaghi , Gian Paolo Clemente , Rosanna Grassi

This paper investigates the spread of infectious diseases within a networked community by integrating epidemic transmission and public opinion dynamics. We propose a novel discrete-time networked SIS (Susceptible-Infectious-Susceptible)…

系统与控制 · 电气工程与系统科学 2025-08-14 Qiulin Xu , Tatsuya Masada , Hideaki Ishii

The contact process, or SIS epidemic, is a continuous-time Markov process used to model the spread of infection on a graph. Each vertex is either healthy or infected, and each infected vertex independently infects each of its healthy…

概率论 · 数学 2025-12-09 Shirshendu Chatterjee , David Sivakoff , Matthew Wascher

Disease awareness in epidemiology can be modelled with adaptive contact networks, where the interplay of disease dynamics and network alteration often adds new phases to the standard models (Gross et al. 2006, Shaw et al. 2008) and, in…

适应与自组织系统 · 物理学 2012-12-06 Stefan Wieland , Tomas Aquino , Andrea Parisi , Ana Nunes

This paper conducts research on the established model and presents the main conclusions . Firstly, by separately considering the infectivity of each of the two infectious diseases and the infectivity of the population simultaneously…

种群与进化 · 定量生物学 2025-03-13 Yang Liu

Whether it be physical, biological or social processes, complex systems exhibit dynamics that are exceedingly difficult to understand or predict from underlying principles. Here we report a striking correspondence between the collective…

原子物理 · 物理学 2021-05-19 T. M. Wintermantel , M. Buchhold , S. Shevate , M. Morgado , Y. Wang , G. Lochead , S. Diehl , S. Whitlock

The decentralized patch distribution mechanism holds significant promise as an alternative to its centralized counterpart. For the purpose of accurately evaluating the performance of the decentralized patch distribution mechanism and based…

社会与信息网络 · 计算机科学 2023-02-27 Lu-Xing Yang , Xiaofan Yang , Qingyi Zhu , Chenquan Gan

We investigate an epidemic model based on Bailey's continuous differential system. In the continuous time domain, we extend the classical model to time-dependent coefficients and present an alternative solution method to Gleissner's…

经典分析与常微分方程 · 数学 2019-01-01 Martin Bohner , Sabrina Streipert , Delfim F. M. Torres

We study a multi-type SIR epidemic process among a heterogeneous population that interacts through a network. When we base social contact on a random graph with given vertex degrees, we give limit theorems on the fraction of infected…

物理与社会 · 物理学 2021-11-02 Hamed Amini , Andreea Minca

We study an SIS epidemic process over a static contact network where the nodes have partial information about the epidemic state. They react by limiting their interactions with their neighbors when they believe the epidemic is currently…

社会与信息网络 · 计算机科学 2024-05-03 Keith Paarporn , Ceyhun Eksin , Joshua S. Weitz , Jeff S. Shamma

Recent studies on network geometry, a way of describing network structures as geometrical objects, are revolutionizing our way to understand dynamical processes on networked systems. Here, we cope with the problem of epidemic spreading,…

物理与社会 · 物理学 2020-03-04 Joan T. Matamalas , Sergio Gómez , Alex Arenas

This paper examines a susceptible-infected-susceptible (SIS) epidemic reaction-diffusion model with no-flux boundary conditions and constant total population. The infection mechanism in the model is described by a nonlinear term of the form…

偏微分方程分析 · 数学 2024-12-20 Rui Peng , Rachidi B Salako , Yixiang Wu
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