The effect of delay in viral production in within-host models during early infection
Delay in viral production may have a significant impact on the early stages of infection. During the eclipse phase, the time from viral entry until active production of viral particles, no viruses are produced. This delay affects the probability that a viral infection becomes established and timing...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-03-01
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| Series: | Journal of Biological Dynamics |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17513758.2018.1498984 |
| _version_ | 1818529337653067776 |
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| author | Fan Bai Krystin E. S. Huff Linda J. S. Allen |
| author_facet | Fan Bai Krystin E. S. Huff Linda J. S. Allen |
| author_sort | Fan Bai |
| collection | DOAJ |
| description | Delay in viral production may have a significant impact on the early stages of infection. During the eclipse phase, the time from viral entry until active production of viral particles, no viruses are produced. This delay affects the probability that a viral infection becomes established and timing of the peak viral load. Deterministic and stochastic models are formulated with either multiple latent stages or a fixed delay for the eclipse phase. The deterministic model with multiple latent stages approaches in the limit the model with a fixed delay as the number of stages approaches infinity. The deterministic model framework is used to formulate continuous-time Markov chain and stochastic differential equation models. The probability of a minor infection with rapid viral clearance as opposed to a major full-blown infection with a high viral load is estimated from a branching process approximation of the Markov chain model and the results are confirmed through numerical simulations. In addition, parameter values for influenza A are used to numerically estimate the time to peak viral infection and peak viral load for the deterministic and stochastic models. Although the average length of the eclipse phase is the same in each of the models, as the number of latent stages increases, the numerical results show that the time to viral peak and the peak viral load increase. |
| first_indexed | 2024-12-11T17:05:36Z |
| format | Article |
| id | doaj.art-5f3d88c2345d40369f5d753c376d0750 |
| institution | Directory Open Access Journal |
| issn | 1751-3758 1751-3766 |
| language | English |
| last_indexed | 2024-12-11T17:05:36Z |
| publishDate | 2019-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Biological Dynamics |
| spelling | doaj.art-5f3d88c2345d40369f5d753c376d07502022-12-22T00:57:41ZengTaylor & Francis GroupJournal of Biological Dynamics1751-37581751-37662019-03-01130477310.1080/17513758.2018.14989841498984The effect of delay in viral production in within-host models during early infectionFan Bai0Krystin E. S. Huff1Linda J. S. Allen2Texas Tech UniversityTexas Tech UniversityTexas Tech UniversityDelay in viral production may have a significant impact on the early stages of infection. During the eclipse phase, the time from viral entry until active production of viral particles, no viruses are produced. This delay affects the probability that a viral infection becomes established and timing of the peak viral load. Deterministic and stochastic models are formulated with either multiple latent stages or a fixed delay for the eclipse phase. The deterministic model with multiple latent stages approaches in the limit the model with a fixed delay as the number of stages approaches infinity. The deterministic model framework is used to formulate continuous-time Markov chain and stochastic differential equation models. The probability of a minor infection with rapid viral clearance as opposed to a major full-blown infection with a high viral load is estimated from a branching process approximation of the Markov chain model and the results are confirmed through numerical simulations. In addition, parameter values for influenza A are used to numerically estimate the time to peak viral infection and peak viral load for the deterministic and stochastic models. Although the average length of the eclipse phase is the same in each of the models, as the number of latent stages increases, the numerical results show that the time to viral peak and the peak viral load increase.http://dx.doi.org/10.1080/17513758.2018.1498984Markov chainprobability of extinctionstochastic differential equationviral infection |
| spellingShingle | Fan Bai Krystin E. S. Huff Linda J. S. Allen The effect of delay in viral production in within-host models during early infection Journal of Biological Dynamics Markov chain probability of extinction stochastic differential equation viral infection |
| title | The effect of delay in viral production in within-host models during early infection |
| title_full | The effect of delay in viral production in within-host models during early infection |
| title_fullStr | The effect of delay in viral production in within-host models during early infection |
| title_full_unstemmed | The effect of delay in viral production in within-host models during early infection |
| title_short | The effect of delay in viral production in within-host models during early infection |
| title_sort | effect of delay in viral production in within host models during early infection |
| topic | Markov chain probability of extinction stochastic differential equation viral infection |
| url | http://dx.doi.org/10.1080/17513758.2018.1498984 |
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