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Networks of contacts capable of spreading infectious diseases are often observed to be highly heterogeneous, with the majority of individuals having fewer contacts than the mean, and a significant minority having relatively very many…

物理与社会 · 物理学 2016-12-21 César Parra-Rojas , Thomas House , Alan J. McKane

For preventing the spread of epidemics such as the coronavirus disease COVID-19, social distancing and the isolation of infected persons are crucial. However, existing reaction-diffusion equations for epidemic spreading are incapable of…

种群与进化 · 定量生物学 2020-11-18 Michael te Vrugt , Jens Bickmann , Raphael Wittkowski

Mathematical models of epidemics often use compartmental models dividing the population into several compartments. Based on a microscopic setting describing the temporal evolution of the subpopulation sizes in the compartments by stochastic…

种群与进化 · 定量生物学 2025-03-11 Florent Ouabo Kamkumo , Ibrahim Mbouandi Njiasse , Ralf Wunderlich

Most infectious diseases including more than half of known human pathogens are not restricted to just one host, yet much of the mathematical modeling of infections has been limited to a single species. We investigate consequences of a…

种群与进化 · 定量生物学 2017-08-30 Sergei Maslov , Kim Sneppen

We define and study an open stochastic SIR (Susceptible -- Infected -- Removed) model on a graph in order to describe the spread of an epidemic on a cattle trade network with epidemiological and demographic dynamics occurring over the same…

概率论 · 数学 2019-03-28 Pierre Montagnon

Modeling epidemic dynamics plays an important role in studying how diseases spread, predicting their future course, and designing strategies to control them. In this letter, we introduce a model of SIR (susceptible-infected-removed) type…

种群与进化 · 定量生物学 2013-12-17 Li Chen , Fakhteh Ghanbarnejad , Weiran Cai , Peter Grassberger

Motivated by recent epidemic outbreaks, including those of COVID-19, we solve the canonical problem of calculating the dynamics and likelihood of extensive outbreaks in a population within a large class of stochastic epidemic models with…

种群与进化 · 定量生物学 2022-01-31 Jason Hindes , Michael Assaf , Ira B. Schwartz

This paper is concerned with a stochastic model for the spread of an SEIR (susceptible -> exposed (=latent) -> infective -> removed) epidemic with a contact tracing scheme, in which removed individuals may name some of their infectious…

概率论 · 数学 2015-12-08 Frank G Ball , Edward S Knock , Philip D O'Neill

Epidemic models currently play a central role in our attempts to understand and control infectious diseases. Here, we derive a model for the diffusion limit of stochastic susceptible-infectious-removed (SIR) epidemic dynamics on a…

种群与进化 · 定量生物学 2013-09-30 Matthew Graham , Thomas House

We study the spread of stochastic SIR (Susceptible $\to$ Infectious $\to$ Recovered) epidemics in two types of structured populations, both consisting of schools and households. In each of the types, every individual is part of one school…

物理与社会 · 物理学 2015-03-23 Tanneke Ouboter , Ronald Meester , Pieter Trapman

The impact of spatial structure on the spread of an epidemic is an important issue in the propagation of infectious diseases. Recent studies, both deterministic and stochastic, have made it possible to understand the importance of the…

概率论 · 数学 2023-01-09 Alphonse Emakoua

Susceptible-Infected-Recovered (SIR) models have been used for decades to understand epidemic outbreak dynamics. We develop an SIR model specifically designed to study the effects of population behavior with respect to health and…

种群与进化 · 定量生物学 2023-08-03 Jasmin Nunuvero , Angelique Santiago , Moshe Cohen , Anca Radulescu

We study the spread of susceptible-infected-recovered (SIR) infectious diseases where an individual's infectiousness and probability of recovery depend on his/her "age" of infection. We focus first on early outbreak stages when stochastic…

种群与进化 · 定量生物学 2009-05-14 Joel Miller , Bahman Davoudi , Rafael Meza , Anja Slim , Babak Pourbohloul

We introduce a modified SIR model with memory for the dynamics of epidemic spreading in a constant population of individuals. Each individual is in one of the states susceptible (${\bf S}$), infected (${\bf I}$) or recovered (${\bf R}$). In…

This paper deals with a new epidemiological model of SIRS with stochastic perturbations. The primary objective is to establish the existence of a unique non-negative nonlocal solution. Using the basic reproduction number $\mathscr{R}_0$…

动力系统 · 数学 2025-03-10 Achraf Zinihi , Moulay Rchid Sidi Ammi , Matthias Ehrhardt

We develop a stochastic two-patch epidemic model with nonlinear recidivism to investigate infectious disease dynamics in heterogeneous populations. Extending a deterministic framework, we introduce stochasticity to account for random…

种群与进化 · 定量生物学 2024-05-21 Juan G. Calvo , Mario I. Simoy , Juan P. Aparicio , José E. Chacón , Fabio Sanchez

With the premise that social interactions are described by power-law distributions, we study a SIR stochastic dynamic on a static scale-free random network generated via configuration model. We verify our model with respect to deterministic…

物理与社会 · 物理学 2024-05-10 Sara Sottile , Ozan Kahramanoğulları , Mattia Sensi

In a metapopulation network, infectious diseases spread widely because of the travel of individuals. In the present study, we consider a modified metapopulation Susceptible-Infected-Removed (SIR) model with a latent period, which we call…

物理与社会 · 物理学 2017-08-02 Kazue Kudo , Kanako Mizuno

We study a multilayer SIR model with two levels of mixing, namely a global level which is uniformly mixing, and a local level with two layers distinguishing household and workplace contacts, respectively. We establish the large population…

概率论 · 数学 2023-10-27 Madeleine Kubasch

The effect of spatial correlations on the spread of infectious diseases was investigated using a stochastic SIR (Susceptible-Infective-Recovered) model on complex networks. It was found that in addition to the reduction of the effective…

种群与进化 · 定量生物学 2007-05-23 J. Verdasca , M. M. Telo da Gama , A. Nunes , N. R. Bernardino , J. M. Pacheco , M. C. Gomes
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