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Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics
Mathematical Modelling of Natural Phenomena, Volume: 12, Issue: 5, Pages: 63 - 77
Swansea University Author:
Igor Sazonov
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DOI (Published version): 10.1051/mmnp/201712505
Abstract
Many human infections with viruses such as human immunodeficiency virus type 1 (HIV--1) are characterized by low numbers of founder viruses for which the random effects and discrete nature of populations have a strong effect on the dynamics, e.g., extinction versus spread. It remains to be establish...
Published in: | Mathematical Modelling of Natural Phenomena |
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ISSN: | 1760-6101 |
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2017
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URI: | https://cronfa.swan.ac.uk/Record/cronfa36270 |
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2017-12-11T15:07:40.3526693 v2 36270 2017-10-26 Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics 05a507952e26462561085fb6f62c8897 0000-0001-6685-2351 Igor Sazonov Igor Sazonov true false 2017-10-26 AERO Many human infections with viruses such as human immunodeficiency virus type 1 (HIV--1) are characterized by low numbers of founder viruses for which the random effects and discrete nature of populations have a strong effect on the dynamics, e.g., extinction versus spread. It remains to be established whether HIV transmission is a stochastic process on the whole. In this study, we consider the simplest (so-called, 'consensus') virus dynamics model and develop a computational methodology for building an equivalent stochastic model based on Markov Chain accounting for random interactions between the components. The model is used to study the evolution of the probability densities for the virus and target cell populations. It predicts the probability of infection spread as a function of the number of the transmitted viruses. A hybrid algorithm is suggested to compute efficiently the dynamics in state space domain characterized by a mix of small and large species densities. Journal Article Mathematical Modelling of Natural Phenomena 12 5 63 77 1760-6101 mathematical model, virus infection, stochastic dynamics, Markov Chain, hybrid modelling 31 12 2017 2017-12-31 10.1051/mmnp/201712505 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2017-12-11T15:07:40.3526693 2017-10-26T09:41:12.1455122 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering I. Sazonov 1 D. Grebennikov 2 M. Kelbert 3 G. Bocharov 4 Igor Sazonov 0000-0001-6685-2351 5 0036270-31102017091719.pdf sazonov2017(4).pdf 2017-10-31T09:17:19.5400000 Output 897963 application/pdf Version of Record true 2017-10-31T00:00:00.0000000 true eng |
title |
Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics |
spellingShingle |
Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics Igor Sazonov |
title_short |
Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics |
title_full |
Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics |
title_fullStr |
Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics |
title_full_unstemmed |
Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics |
title_sort |
Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics |
author_id_str_mv |
05a507952e26462561085fb6f62c8897 |
author_id_fullname_str_mv |
05a507952e26462561085fb6f62c8897_***_Igor Sazonov |
author |
Igor Sazonov |
author2 |
I. Sazonov D. Grebennikov M. Kelbert G. Bocharov Igor Sazonov |
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Journal article |
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Mathematical Modelling of Natural Phenomena |
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12 |
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2017 |
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Swansea University |
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1760-6101 |
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10.1051/mmnp/201712505 |
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Faculty of Science and Engineering |
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School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering |
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description |
Many human infections with viruses such as human immunodeficiency virus type 1 (HIV--1) are characterized by low numbers of founder viruses for which the random effects and discrete nature of populations have a strong effect on the dynamics, e.g., extinction versus spread. It remains to be established whether HIV transmission is a stochastic process on the whole. In this study, we consider the simplest (so-called, 'consensus') virus dynamics model and develop a computational methodology for building an equivalent stochastic model based on Markov Chain accounting for random interactions between the components. The model is used to study the evolution of the probability densities for the virus and target cell populations. It predicts the probability of infection spread as a function of the number of the transmitted viruses. A hybrid algorithm is suggested to compute efficiently the dynamics in state space domain characterized by a mix of small and large species densities. |
published_date |
2017-12-31T03:45:18Z |
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1763752141828128768 |
score |
11.021648 |