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Probing a Stochastic Epidemic Hepatitis C Virus Model with a Chronically Infected Treated Population
The hepatitis C virus is hitherto a tremendous threat to human beings, but many researchers have analyzed mathematical models for hepatitis C virus transmission dynamics only in the deterministic case. Stochasticity plays an immense role in pathology and epidemiology. Hence, the main theme of this a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310688/ https://www.ncbi.nlm.nih.gov/pubmed/35911571 http://dx.doi.org/10.1007/s10473-022-0521-1 |
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author | Rajasekar, S. P. Pitchaimani, M. Zhu, Quanxin |
author_facet | Rajasekar, S. P. Pitchaimani, M. Zhu, Quanxin |
author_sort | Rajasekar, S. P. |
collection | PubMed |
description | The hepatitis C virus is hitherto a tremendous threat to human beings, but many researchers have analyzed mathematical models for hepatitis C virus transmission dynamics only in the deterministic case. Stochasticity plays an immense role in pathology and epidemiology. Hence, the main theme of this article is to investigate a stochastic epidemic hepatitis C virus model with five states of epidemiological classification: susceptible, acutely infected, chronically infected, recovered or removed and chronically infected, and treated. The stochastic hepatitis C virus model in epidemiology is established based on the environmental influence on individuals, is manifested by stochastic perturbations, and is proportional to each state. We assert that the stochastic HCV model has a unique global positive solution and attains sufficient conditions for the extinction of the hepatotropic RNA virus. Furthermore, by constructing a suitable Lyapunov function, we obtain sufficient conditions for the existence of an ergodic stationary distribution of the solutions to the stochastic HCV model. Moreover, this article confirms that using numerical simulations, the six parameters of the stochastic HCV model can have a high impact over the disease transmission dynamics, specifically the disease transmission rate, the rate of chronically infected population, the rate of progression to chronic infection, the treatment failure rate of chronically infected population, the recovery rate from chronic infection and the treatment rate of the chronically infected population. Eventually, numerical simulations validate the effectiveness of our theoretical conclusions. |
format | Online Article Text |
id | pubmed-9310688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-93106882022-07-26 Probing a Stochastic Epidemic Hepatitis C Virus Model with a Chronically Infected Treated Population Rajasekar, S. P. Pitchaimani, M. Zhu, Quanxin Acta Math Sci Article The hepatitis C virus is hitherto a tremendous threat to human beings, but many researchers have analyzed mathematical models for hepatitis C virus transmission dynamics only in the deterministic case. Stochasticity plays an immense role in pathology and epidemiology. Hence, the main theme of this article is to investigate a stochastic epidemic hepatitis C virus model with five states of epidemiological classification: susceptible, acutely infected, chronically infected, recovered or removed and chronically infected, and treated. The stochastic hepatitis C virus model in epidemiology is established based on the environmental influence on individuals, is manifested by stochastic perturbations, and is proportional to each state. We assert that the stochastic HCV model has a unique global positive solution and attains sufficient conditions for the extinction of the hepatotropic RNA virus. Furthermore, by constructing a suitable Lyapunov function, we obtain sufficient conditions for the existence of an ergodic stationary distribution of the solutions to the stochastic HCV model. Moreover, this article confirms that using numerical simulations, the six parameters of the stochastic HCV model can have a high impact over the disease transmission dynamics, specifically the disease transmission rate, the rate of chronically infected population, the rate of progression to chronic infection, the treatment failure rate of chronically infected population, the recovery rate from chronic infection and the treatment rate of the chronically infected population. Eventually, numerical simulations validate the effectiveness of our theoretical conclusions. Springer Nature Singapore 2022-07-25 2022 /pmc/articles/PMC9310688/ /pubmed/35911571 http://dx.doi.org/10.1007/s10473-022-0521-1 Text en © Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Rajasekar, S. P. Pitchaimani, M. Zhu, Quanxin Probing a Stochastic Epidemic Hepatitis C Virus Model with a Chronically Infected Treated Population |
title | Probing a Stochastic Epidemic Hepatitis C Virus Model with a Chronically Infected Treated Population |
title_full | Probing a Stochastic Epidemic Hepatitis C Virus Model with a Chronically Infected Treated Population |
title_fullStr | Probing a Stochastic Epidemic Hepatitis C Virus Model with a Chronically Infected Treated Population |
title_full_unstemmed | Probing a Stochastic Epidemic Hepatitis C Virus Model with a Chronically Infected Treated Population |
title_short | Probing a Stochastic Epidemic Hepatitis C Virus Model with a Chronically Infected Treated Population |
title_sort | probing a stochastic epidemic hepatitis c virus model with a chronically infected treated population |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310688/ https://www.ncbi.nlm.nih.gov/pubmed/35911571 http://dx.doi.org/10.1007/s10473-022-0521-1 |
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