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dc.creatorPinky, Lubna
dc.creatorGonzález-Parra, Gilberto
dc.creatorDobrovolny, Hana M.
dc.date.accessioned2019-07-11T15:17:10Z
dc.date.available2019-07-11T15:17:10Z
dc.date.issued2019-04-16
dc.identifier.urihttps://doi.org/10.1186/s12859-019-2793-6
dc.identifier.urihttps://repository.tcu.edu/handle/116099117/26383
dc.identifier.urihttps://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2793-6
dc.description.abstractBackground: Respiratory viral infections are a leading cause of mortality worldwide. As many as 40% of patients hospitalized with influenza-like illness are reported to be infected with more than one type of virus. However, it is not clear whether these infections are more severe than single viral infections. Mathematical models can be used to help us understand the dynamics of respiratory viral coinfections and their impact on the severity of the illness. Most models of viral infections use ordinary differential equations (ODE) that reproduce the average behavior of the infection, however, they might be inaccurate in predicting certain events because of the stochastic nature of viral replication cycle. Stochastic simulations of single virus infections have shown that there is an extinction probability that depends on the size of the initial viral inoculum and parameters that describe virus-cell interactions. Thus the coinfection dynamics predicted by the ODE might be difficult to observe in reality. Results: In this work, a continuous-time Markov chain (CTMC) model is formulated to investigate probabilistic outcomes of coinfections. This CTMC model is based on our previous coinfection model, expressed in terms of a system of ordinary differential equations. Using the Gillespie method for stochastic simulation, we examine whether stochastic effects early in the infection can alter which virus dominates the infection. Conclusions: We derive extinction probabilities for each virus individually as well as for the infection as a whole. We find that unlike the prediction of the ODE model, for similar initial growth rates stochasticity allows for a slower growing virus to out-compete a faster growing virus.en_US
dc.language.isoenen_US
dc.publisherBioMed Central
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceBMC Bioinformatics
dc.subjectViral coinfectionen_US
dc.subjectRespiratory virusen_US
dc.subjectWithin-host modelen_US
dc.subjectContinuous-timeen_US
dc.subjectMarkov chainen_US
dc.subjectMulti-type branching processen_US
dc.subjectExtinction probabilityen_US
dc.titleEffect of stochasticity on coinfection dynamics of respiratory viruses
dc.typeArticleen_US
dc.rights.holderPinky et al.
dc.rights.licenseCC BY 4.0
local.collegeCollege of Science and Engineering
local.departmentPhysics and Astronomy
local.personsPinky, Dobrovolny (PHYS)


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