On a Novel SVEIRS Markov chain epidemic model with multiple discrete delays and infection rates: modeling and sensitivity analysis to determine vaccination effects

Divine Wanduku; Omotomilola Jegede; Chinmoy Rahul; Broderick Oluyede; Oluwaseun Farotimi

doi:10.1016/B978-0-32-399557-3.00009-0

On a Novel SVEIRS Markov chain epidemic model with multiple discrete delays and infection rates: modeling and sensitivity analysis to determine vaccination effects

Divine Wanduku
, Omotomilola Jegede
, Chinmoy Rahul
, Broderick Oluyede
, Oluwaseun Farotimi

Mathematical Sciences

Georgia Southern University

Research output: Contribution to book or proceeding › Chapter › peer-review

3 Scopus citations

Abstract

A novel discrete time general Markov chain SVEIRS (Susceptible-Vaccinated-Exposed-Infected-Recovered-Susceptible) epidemic model is derived and studied. The model incorporates finite delay times for disease incubation, natural immunity, artificial immunity, and the period of infectiousness of infected individuals. The novel platform for representing different states of the disease in the population utilizes two discrete time measures for the current time of a person's state and also how long a person has been in the current state. Two submodels are derived based on whether the drive to get vaccinated is inspired by close contacts with infectious individuals or otherwise. Sensitivity analysis is conducted on the two submodels to determine how vaccination affects disease eradication.

Original language	English
Title of host publication	Advances in Epidemiological Modeling and Control of Viruses
Publisher	Elsevier
Pages	95-143
Number of pages	49
ISBN (Electronic)	9780323995573
ISBN (Print)	9780323995580
DOIs	https://doi.org/10.1016/B978-0-32-399557-3.00009-0
State	Published - Jan 1 2023

Publication series

Name	Advances in Epidemiological Modeling and Control of Viruses

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Scopus Subject Areas

General Economics, Econometrics and Finance
General Business, Management and Accounting

Keywords

Chain-binomial models
Correlated vaccination-infection rates
Discrete time delays
Markov chains
SVEIRS epidemic model
Vaccination impacts

Access to Document

10.1016/B978-0-32-399557-3.00009-0

Cite this

Wanduku, D., Jegede, O., Rahul, C., Oluyede, B., & Farotimi, O. (2023). On a Novel SVEIRS Markov chain epidemic model with multiple discrete delays and infection rates: modeling and sensitivity analysis to determine vaccination effects. In Advances in Epidemiological Modeling and Control of Viruses (pp. 95-143). (Advances in Epidemiological Modeling and Control of Viruses). Elsevier. https://doi.org/10.1016/B978-0-32-399557-3.00009-0

Wanduku, Divine ; Jegede, Omotomilola ; Rahul, Chinmoy et al. / On a Novel SVEIRS Markov chain epidemic model with multiple discrete delays and infection rates : modeling and sensitivity analysis to determine vaccination effects. Advances in Epidemiological Modeling and Control of Viruses. Elsevier, 2023. pp. 95-143 (Advances in Epidemiological Modeling and Control of Viruses).

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abstract = "A novel discrete time general Markov chain SVEIRS (Susceptible-Vaccinated-Exposed-Infected-Recovered-Susceptible) epidemic model is derived and studied. The model incorporates finite delay times for disease incubation, natural immunity, artificial immunity, and the period of infectiousness of infected individuals. The novel platform for representing different states of the disease in the population utilizes two discrete time measures for the current time of a person's state and also how long a person has been in the current state. Two submodels are derived based on whether the drive to get vaccinated is inspired by close contacts with infectious individuals or otherwise. Sensitivity analysis is conducted on the two submodels to determine how vaccination affects disease eradication.",

keywords = "Chain-binomial models, Correlated vaccination-infection rates, Discrete time delays, Markov chains, SVEIRS epidemic model, Vaccination impacts",

author = "Divine Wanduku and Omotomilola Jegede and Chinmoy Rahul and Broderick Oluyede and Oluwaseun Farotimi",

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Wanduku, D, Jegede, O, Rahul, C, Oluyede, B & Farotimi, O 2023, On a Novel SVEIRS Markov chain epidemic model with multiple discrete delays and infection rates: modeling and sensitivity analysis to determine vaccination effects. in Advances in Epidemiological Modeling and Control of Viruses. Advances in Epidemiological Modeling and Control of Viruses, Elsevier, pp. 95-143. https://doi.org/10.1016/B978-0-32-399557-3.00009-0

On a Novel SVEIRS Markov chain epidemic model with multiple discrete delays and infection rates: modeling and sensitivity analysis to determine vaccination effects. / Wanduku, Divine; Jegede, Omotomilola; Rahul, Chinmoy et al.
Advances in Epidemiological Modeling and Control of Viruses. Elsevier, 2023. p. 95-143 (Advances in Epidemiological Modeling and Control of Viruses).

Research output: Contribution to book or proceeding › Chapter › peer-review

TY - CHAP

T1 - On a Novel SVEIRS Markov chain epidemic model with multiple discrete delays and infection rates

T2 - modeling and sensitivity analysis to determine vaccination effects

AU - Wanduku, Divine

AU - Jegede, Omotomilola

AU - Rahul, Chinmoy

AU - Oluyede, Broderick

AU - Farotimi, Oluwaseun

PY - 2023/1/1

Y1 - 2023/1/1

N2 - A novel discrete time general Markov chain SVEIRS (Susceptible-Vaccinated-Exposed-Infected-Recovered-Susceptible) epidemic model is derived and studied. The model incorporates finite delay times for disease incubation, natural immunity, artificial immunity, and the period of infectiousness of infected individuals. The novel platform for representing different states of the disease in the population utilizes two discrete time measures for the current time of a person's state and also how long a person has been in the current state. Two submodels are derived based on whether the drive to get vaccinated is inspired by close contacts with infectious individuals or otherwise. Sensitivity analysis is conducted on the two submodels to determine how vaccination affects disease eradication.

AB - A novel discrete time general Markov chain SVEIRS (Susceptible-Vaccinated-Exposed-Infected-Recovered-Susceptible) epidemic model is derived and studied. The model incorporates finite delay times for disease incubation, natural immunity, artificial immunity, and the period of infectiousness of infected individuals. The novel platform for representing different states of the disease in the population utilizes two discrete time measures for the current time of a person's state and also how long a person has been in the current state. Two submodels are derived based on whether the drive to get vaccinated is inspired by close contacts with infectious individuals or otherwise. Sensitivity analysis is conducted on the two submodels to determine how vaccination affects disease eradication.

KW - Chain-binomial models

KW - Correlated vaccination-infection rates

KW - Discrete time delays

KW - Markov chains

KW - SVEIRS epidemic model

KW - Vaccination impacts

UR - https://www.scopus.com/pages/publications/85151625420

U2 - 10.1016/B978-0-32-399557-3.00009-0

DO - 10.1016/B978-0-32-399557-3.00009-0

M3 - Chapter

AN - SCOPUS:85151625420

SN - 9780323995580

T3 - Advances in Epidemiological Modeling and Control of Viruses

SP - 95

EP - 143

BT - Advances in Epidemiological Modeling and Control of Viruses

PB - Elsevier

ER -

Wanduku D, Jegede O, Rahul C, Oluyede B, Farotimi O. On a Novel SVEIRS Markov chain epidemic model with multiple discrete delays and infection rates: modeling and sensitivity analysis to determine vaccination effects. In Advances in Epidemiological Modeling and Control of Viruses. Elsevier. 2023. p. 95-143. (Advances in Epidemiological Modeling and Control of Viruses). doi: 10.1016/B978-0-32-399557-3.00009-0

On a Novel SVEIRS Markov chain epidemic model with multiple discrete delays and infection rates: modeling and sensitivity analysis to determine vaccination effects

Abstract

Publication series

UN SDGs

Scopus Subject Areas

Keywords

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