Results 1 to 10 of about 70,485 (205)

Solutions of Conformable Fractional-Order SIR Epidemic Model [PDF]

International Journal of Differential Equations, 2021
In this paper, the conformable fractional-order SIR epidemic model are solved by means of an analytic technique for nonlinear problems, namely, the conformable fractional differential transformation method (CFDTM) and variational iteration method (VIM ...
Atimad Harir, Said Malliani, Lalla Saadia Chandli   +2 more
doaj   +2 more sources

Dynamics Analysis of a Stochastic SIR Epidemic Model [PDF]

Abstract and Applied Analysis, 2014
We investigate an SIR epidemic model with stochastic perturbations. We assume that stochastic perturbations are of a white noise type which is directly proportional to the distances of three variables from the steady-state values, respectively.
Feng Rao
doaj   +4 more sources

A SIR Epidemic Model Allowing Recovery

Axioms
The deterministic SIR model for disease spread in a closed population is extended to allow infected individuals to recover to the susceptible state. This extension preserves the second constant of motion, i.e., a functional relationship of susceptible ...
Anthony G. Pakes
doaj   +2 more sources

Efficient Numerical Solutions to a SIR Epidemic Model

Mathematics, 2022
Two non-standard predictor-corrector type finite difference methods for a SIR epidemic model are proposed. The methods have useful and significant features, such as positivity, basic stability, boundedness and preservation of the conservation laws.
Mohammad Mehdizadeh Khalsaraei, Ali Shokri, Higinio Ramos, Shao-Wen Yao, Maryam Molayi   +4 more
doaj   +3 more sources

Stability on coupling SIR epidemic model with vaccination [PDF]

Journal of Applied Mathematics, 2005
We develop a mathematical model for the disease which can be transmitted via vector and through blood transfusion in host population. The host population is structured by the chronological age.
Helong Liu, Houbao Xu, Jingyuan Yu, Guangtian Zhu   +3 more
doaj   +4 more sources

Assessing bias in susceptible–infected–recovered estimation from aggregated epidemic data [PDF]

Royal Society Open Science
The canonical susceptible–infected–recovered (SIR) epidemic model is ubiquitous in assessing severity to guide interventions. It is typically applied to hierarchically aggregated data from distinct sub-regions.
Naijian Shen, Lydia Bourouiba
doaj   +2 more sources

Inferring structure and parameters of stochastic reaction networks with logistic regression. [PDF]

PLoS ONE
Identifying network structure and estimating reaction parameters remain central challenges in modeling chemical reaction networks. In this work, we develop likelihood-based methods that use multinomial logistic regression to infer both stoichiometries ...
Boseung Choi, Hye-Won Kang, Grzegorz A Rempala   +2 more
doaj   +2 more sources

Numerical Solution of an Interval-Based Uncertain SIR (Susceptible–Infected–Recovered) Epidemic Model by Homotopy Analysis Method

Axioms, 2021
This work proposes an interval-based uncertain Susceptible–Infected–Recovered (SIR) epidemic model. The interval model has been numerically solved by the homotopy analysis method (HAM).
Emmanuel A. Bakare, Snehashish Chakraverty, Radovan Potucek   +2 more
doaj   +1 more source

A real-world data validation of the value of early-stage SIR modelling to public health

Scientific Reports, 2023
Performance of Susceptible-Infected-Recovered (SIR) model in the early stage of a novel epidemic may be hindered by data availability. Additionally, the traditional SIR model may oversimplify the disease progress, and knowledge about the virus and ...
Taoran Liu, Jian Huang, Zonglin He, Yin Zhang, Ni Yan, Casper J. P. Zhang, Wai-Kit Ming   +6 more
doaj   +1 more source

Limits of epidemic prediction using SIR models

Journal of Mathematical Biology, 2022
The Susceptible-Infectious-Recovered (SIR) equations and their extensions comprise a commonly utilized set of models for understanding and predicting the course of an epidemic. In practice, it is of substantial interest to estimate the model parameters based on noisy observations early in the outbreak, well before the epidemic reaches its peak.
Omar Melikechi, Alexander L. Young, Tao Tang, Trevor Bowman, David Dunson, James Johndrow   +5 more
openaire   +4 more sources