The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay

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The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay. / Apenteng, Ofosuhene O.; Ismail, Noor Azina.

I: PLoS ONE, Bind 9, Nr. 6, 98288, 09.06.2014.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Apenteng, OO & Ismail, NA 2014, 'The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay', PLoS ONE, bind 9, nr. 6, 98288. https://doi.org/10.1371/journal.pone.0098288

APA

Apenteng, O. O., & Ismail, N. A. (2014). The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay. PLoS ONE, 9(6), [98288]. https://doi.org/10.1371/journal.pone.0098288

Vancouver

Apenteng OO, Ismail NA. The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay. PLoS ONE. 2014 jun. 9;9(6). 98288. https://doi.org/10.1371/journal.pone.0098288

Author

Apenteng, Ofosuhene O. ; Ismail, Noor Azina. / The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay. I: PLoS ONE. 2014 ; Bind 9, Nr. 6.

Bibtex

@article{aaf239aefdbb4645be38d33ec12af0f2,
title = "The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay",
abstract = "Previous models of disease spread involving delay have used basic SIR (susceptible - infectious - recovery) formulae and approaches. This paper demonstrates how time-varying SEIRS (S - exposed -I -R -S) models can be extended with delay to produce wave propagations that simulate periodic wave fronts of disease spread in the context of population movements. The model also takes into account the natural mortality associated with the disease spread. Understanding the delay of an infectious disease is critical when attempting to predict where and how fast the disease will propagate. We use cellular automata to model the delay and its effect on the spread of infectious diseases where population movement occurs. We illustrate an approach using wavelet transform analysis to understand the impact of the delay on the spread of infectious diseases. The results indicate that including delay provides novel ways to understand the effects of migration and population movement on disease spread.",
keywords = "EPIDEMIC MODEL",
author = "Apenteng, {Ofosuhene O.} and Ismail, {Noor Azina}",
year = "2014",
month = jun,
day = "9",
doi = "10.1371/journal.pone.0098288",
language = "English",
volume = "9",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "6",

}

RIS

TY - JOUR

T1 - The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay

AU - Apenteng, Ofosuhene O.

AU - Ismail, Noor Azina

PY - 2014/6/9

Y1 - 2014/6/9

N2 - Previous models of disease spread involving delay have used basic SIR (susceptible - infectious - recovery) formulae and approaches. This paper demonstrates how time-varying SEIRS (S - exposed -I -R -S) models can be extended with delay to produce wave propagations that simulate periodic wave fronts of disease spread in the context of population movements. The model also takes into account the natural mortality associated with the disease spread. Understanding the delay of an infectious disease is critical when attempting to predict where and how fast the disease will propagate. We use cellular automata to model the delay and its effect on the spread of infectious diseases where population movement occurs. We illustrate an approach using wavelet transform analysis to understand the impact of the delay on the spread of infectious diseases. The results indicate that including delay provides novel ways to understand the effects of migration and population movement on disease spread.

AB - Previous models of disease spread involving delay have used basic SIR (susceptible - infectious - recovery) formulae and approaches. This paper demonstrates how time-varying SEIRS (S - exposed -I -R -S) models can be extended with delay to produce wave propagations that simulate periodic wave fronts of disease spread in the context of population movements. The model also takes into account the natural mortality associated with the disease spread. Understanding the delay of an infectious disease is critical when attempting to predict where and how fast the disease will propagate. We use cellular automata to model the delay and its effect on the spread of infectious diseases where population movement occurs. We illustrate an approach using wavelet transform analysis to understand the impact of the delay on the spread of infectious diseases. The results indicate that including delay provides novel ways to understand the effects of migration and population movement on disease spread.

KW - EPIDEMIC MODEL

U2 - 10.1371/journal.pone.0098288

DO - 10.1371/journal.pone.0098288

M3 - Journal article

VL - 9

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 6

M1 - 98288

ER -

ID: 334333863