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Stochastic SEIR Model for Measles with Differential Transformation Method (DTM)

by Hammad Khalid, Hamna Khalid, Maira Shafiq
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 2
Year of Publication: 2022
Authors: Hammad Khalid, Hamna Khalid, Maira Shafiq
10.5120/ijca2022922013

Hammad Khalid, Hamna Khalid, Maira Shafiq . Stochastic SEIR Model for Measles with Differential Transformation Method (DTM). International Journal of Computer Applications. 184, 2 ( Mar 2022), 9-13. DOI=10.5120/ijca2022922013

@article{ 10.5120/ijca2022922013,
author = { Hammad Khalid, Hamna Khalid, Maira Shafiq },
title = { Stochastic SEIR Model for Measles with Differential Transformation Method (DTM) },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2022 },
volume = { 184 },
number = { 2 },
month = { Mar },
year = { 2022 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number2/32303-2022922013/ },
doi = { 10.5120/ijca2022922013 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:20:23.031574+05:30
%A Hammad Khalid
%A Hamna Khalid
%A Maira Shafiq
%T Stochastic SEIR Model for Measles with Differential Transformation Method (DTM)
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 2
%P 9-13
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The epidemic models are important to study and control epidemiological diseases. The SEIR model of measles disease in children has been solved and evaluated using the differential transformation method (DTM). This is a numerical and semi-analytic technique that is used to solve differential and integral equations.We solved a measles model with the help of differential transformation method and discussed two cases one is endemic and other is a disease-free state. In both cases, if threshold number R0 < 1 then we get a locally stable situation while at R0 > 1 we get a locally unstable situation. Also, compared it with [6] to prove better convergence of differential transformation method. This approach offers solutions for converging a series of conveniently computable components. DTM is an efficient tool to solve linear and non-linear differential equations. We compare results with Runge-Kutta fourth order. DTM is much convenient and gives better results.

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Index Terms

Computer Science
Information Sciences

Keywords

Deterministic and stochastic modeling numerical method differential transformation method solution of the measles SEIR model

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