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We propose a simple SIR model in order to investigate the impact of various confinement strategies on a most virulent epidemic. Our approach is motivated by the current COVID-19 pandemic. The main hypothesis is the existence of two…

种群与进化 · 定量生物学 2020-12-30 G. Nakamura , B. Grammaticos , M. Badoual

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

We consider the SIR model and study the first time the number of infected individuals begins to decrease and the first time this population is below a given threshold. We interpret these times as functions of the initial susceptible and…

经典分析与常微分方程 · 数学 2020-09-23 Ryan Hynd , Dennis Ikpe , Terrance Pendleton

We present two epidemiological models, which extend the classical SEIR model by accounting for the effect of indiscriminate quarantining, isolation of infected individuals based on testing and the presence of asymptomatic individuals. Given…

动力系统 · 数学 2020-11-30 Zuzana Chladná , Jana Kopfová , Dmitrii Rachinskii , Pavel Štepánek

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

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

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

This paper analyses an SIRS-type model for infectious diseases with account for behavioural changes associated with the simultaneous spread of awareness in the population. Two types of awareness are included into the model: private…

种群与进化 · 定量生物学 2017-02-17 G. O. Agaba , Y. N. Kyrychko , K. B. Blyuss

This paper focuses on and analyzes realistic SIR models that take stochasticity into account. The proposed systems are applicable to most incidence rates that are used in the literature including the bilinear incidence rate, the…

概率论 · 数学 2024-07-10 Nguyen Du , Alexandru Hening , Nhu Nguyen , George Yin

Traditional biomedical approaches treat diseases in isolation, but the importance of synergistic disease interactions is now recognized. As a first step we present and analyze a simple coinfection model for two diseases affecting…

动力系统 · 数学 2015-04-21 Marcos Marvá , Ezio Venturino , Rafael Bravo de la Parra

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

The Susceptible-Infected-Recovered (SIR) model has successfully mimicked the propagation of such airborne diseases as influenza A (H1N1). Although the SIR model has recently been studied in a multilayer networks configuration, in almost all…

物理与社会 · 物理学 2015-07-16 L. G. Alvarez Zuzek , H. E. Stanley , L. A. Braunstein

We study a symmetric two-disease SIR co-infection model on networks in which co-infected individuals recover at a rate distinct from that of single infections. The model explicitly represents all co-infection states and features absorbing…

Our study is based on an epidemiological compartmental model, the SIRS model. In the SIRS model, each individual is in one of the states susceptible (S), infected(I) or recovered (R), depending on its state of health. In compartment R, an…

种群与进化 · 定量生物学 2023-05-10 Michael Bestehorn , Thomas M. Michelitsch

I estimate the Susceptible-Infected-Recovered (SIR) epidemic model for Coronavirus Disease 2019 (COVID-19). The transmission rate is heterogeneous across countries and far exceeds the recovery rate, which enables a fast spread. In the…

种群与进化 · 定量生物学 2020-03-27 Alexis Akira Toda

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 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

Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. Susceptible-Infectious-Recovered (SIR) models are traditionally used to forecast…

统计方法学 · 统计学 2021-03-10 Andrew Giffin , Wenlong Gong , Suman Majumder , Ana G. Rappold , Brian J. Reich , Shu Yang

Using a stochastic Susceptible-Infected-Removed (SIR) meta-population model of disease transmission, we present analytical calculations and numerical simulations dissecting the interplay between stochasticity and the division of a…

种群与进化 · 定量生物学 2020-12-17 Philip Bittihn , Ramin Golestanian

Facing a global epidemic of new infectious diseases such as COVID-19, non-pharmaceutical interventions (NPIs), which reduce transmission rates without medical actions, are being implemented around the world to mitigate spreads. One of the…

种群与进化 · 定量生物学 2021-03-04 Naoya Fujiwara , Tomokatsu Onaga , Takayuki Wada , Shouhei Takeuchi , Junji Seto , Tomoki Nakaya , Kazuyuki Aihara
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