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Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production / Igor Sazonov, Dmitry Grebennikov, Mark Kelbert, Andreas Meyerhans, Gennady Bocharov

Mathematics, Volume: 8, Issue: 8

Swansea University Author: Igor Sazonov

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DOI (Published version): 10.3390/math8081207

Abstract

Many human virus infections including those with the human immunodeficiency virus type 1 (HIV) are initiated by low numbers of founder viruses. Therefore, random effects have a strong influence on the initial infection dynamics, e.g., extinction versus spread. In this study, we considered the simple...

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Published in: Mathematics
ISSN: 2227-7390
Published: MDPI AG 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa55084
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spelling 2020-08-27T11:24:57.4263728 v2 55084 2020-08-27 Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production 05a507952e26462561085fb6f62c8897 0000-0001-6685-2351 Igor Sazonov Igor Sazonov true false 2020-08-27 AERO Many human virus infections including those with the human immunodeficiency virus type 1 (HIV) are initiated by low numbers of founder viruses. Therefore, random effects have a strong influence on the initial infection dynamics, e.g., extinction versus spread. In this study, we considered the simplest (so-called, ‘consensus’) virus dynamics model and incorporated a delay between infection of a cell and virus progeny release from the infected cell. We then developed an equivalent stochastic virus dynamics model that accounts for this delay in the description of the random interactions between the model components. The new model is used to study the statistical characteristics of virus and target cell populations. It predicts the probability of infection spread as a function of the number of transmitted viruses. A hybrid algorithm is suggested to compute efficiently the system dynamics in state space domain characterized by the mix of small and large species densities. Journal Article Mathematics 8 8 MDPI AG 2227-7390 virus dynamics modelling; Markov process with delay; Monte-Carlo method 22 7 2020 2020-07-22 10.3390/math8081207 http://dx.doi.org/10.3390/math8081207 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2020-08-27T11:24:57.4263728 2020-08-27T11:22:49.2457242 Igor Sazonov 0000-0001-6685-2351 1 Dmitry Grebennikov 2 Mark Kelbert 3 Andreas Meyerhans 4 Gennady Bocharov 5 55084__18055__25849938b3064b60ab65c87a861f6248.pdf 55084.pdf 2020-08-27T11:24:22.1286995 Output 5160072 application/pdf Version of Record true This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited false
title Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production
spellingShingle Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production
Igor, Sazonov
title_short Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production
title_full Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production
title_fullStr Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production
title_full_unstemmed Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production
title_sort Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production
author_id_str_mv 05a507952e26462561085fb6f62c8897
author_id_fullname_str_mv 05a507952e26462561085fb6f62c8897_***_Igor, Sazonov
author Igor, Sazonov
author2 Igor Sazonov
Dmitry Grebennikov
Mark Kelbert
Andreas Meyerhans
Gennady Bocharov
format Journal article
container_title Mathematics
container_volume 8
container_issue 8
publishDate 2020
institution Swansea University
issn 2227-7390
doi_str_mv 10.3390/math8081207
publisher MDPI AG
url http://dx.doi.org/10.3390/math8081207
document_store_str 1
active_str 0
description Many human virus infections including those with the human immunodeficiency virus type 1 (HIV) are initiated by low numbers of founder viruses. Therefore, random effects have a strong influence on the initial infection dynamics, e.g., extinction versus spread. In this study, we considered the simplest (so-called, ‘consensus’) virus dynamics model and incorporated a delay between infection of a cell and virus progeny release from the infected cell. We then developed an equivalent stochastic virus dynamics model that accounts for this delay in the description of the random interactions between the model components. The new model is used to study the statistical characteristics of virus and target cell populations. It predicts the probability of infection spread as a function of the number of transmitted viruses. A hybrid algorithm is suggested to compute efficiently the system dynamics in state space domain characterized by the mix of small and large species densities.
published_date 2020-07-22T04:12:08Z
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