Open Journal of Modelling and Simulation

Volume 5, Issue 1 (January 2017)

ISSN Print: 2327-4018   ISSN Online: 2327-4026

Google-based Impact Factor: 0.35  Citations  

Identifying Critical Parameters in SIR Model for Spread of Disease

HTML  XML Download Download as PDF (Size: 2979KB)  PP. 32-46  
DOI: 10.4236/ojmsi.2017.51003    2,862 Downloads   6,775 Views  Citations

ABSTRACT

Calculating analytical approximate solutions for non-linear infectious disease models is a difficult task. Such models often require computational tools to analyse analytical approximate methods which appear in some theoretical and practical applications in systems biology. They represent key critical elements and give some approximate solutions for such systems. The SIR epidemic disease model is given as the non-linear system of ODE’s. Then, we use a proper scaling to reduce the number of parameters. We suggest Elzaki transform method to find analytical approximate solutions for the simplified model. The technique plays an important role in calculating the analytical approximate solutions. The local and global dynamics of the model are also studied. An investigation of the behaviour at infinity was conducted via finding the lines and singular points at infinity. Model dynamic results are computed in numerical simulations using Pplane8 and SimBiology Toolbox for Mathlab. Results provide a good step forward for describing the model dynamics. More interestingly, the simplified model could be accurate, robust, and used by biologists for different purposes such as identifying critical model elements.

Share and Cite:

Khoshnaw, S. , Mohammad, N. and Salih, R. (2017) Identifying Critical Parameters in SIR Model for Spread of Disease. Open Journal of Modelling and Simulation, 5, 32-46. doi: 10.4236/ojmsi.2017.51003.

Cited by

[1] Entropy production and lumping of species can effectively reduce complex cell signaling pathways
Physica Scripta, 2022
[2] A Flexible Rolling Regression Framework for Time-Varying SIRD models: Application to COVID-19
2021
[3] Optimal Transmission Rate in a Basic Three-Compartment Epidemic Model
2021
[4] Transmission mechanism of Novel coronavirus based on SIR model and emergency supplies network's relation
2020
[5] Minimizing cell signalling pathway elements using lumping parameters
2020
[6] Application of fractional derivative on non-linear biochemical reaction models
2020
[7] Analysis coronavirus disease (COVID-19) model using numerical approaches and logistic model
2020
[8] Mathematical modelling for coronavirus disease (COVID-19) in predicting future behaviours and sensitivity analysis
2020
[9] Mathematical modeling for enzyme inhibitors with slow and fast subsystems
2020
[10] A Quantitative and Qualitative Analysis of the COVID–19 Pandemic Model
2020
[11] Model Reduction for Non-linear Protein Translation Pathways Using Slow and Fast Subsystems
2019
[12] Mathematical Modelling for Complex Biochemical Networks and Identification of Fast and Slow Reactions
2019
[13] Using the sir epidemic model to infer the SARS outbreak in Beijing, 2003
2019
[14] Slow and fast subsystems for complex biochemical reactions
2018
[15] Mathematical Model for the Ebola Virus Disease
Journal of Advanced Physics, 2018
[16] Dynamic Analysis of a Predator and Prey Model with Some Computational Simulations
2017
[17] Mathematical modelling of infectious disease models
2017
[18] STUDY OF INFECTIOUS DISEASE COVID-19 BASED ON MATHEMATICAL MODELING AND IMPACT OF LOCKDOWN IN INDIA

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.