Identifiability of parameters in mathematical models of SARS-CoV-2 infections in humans

dc.contributor.authorCiupe, Stanca M.en
dc.contributor.authorTuncer, Necibeen
dc.date.accessioned2022-10-14T13:41:19Zen
dc.date.available2022-10-14T13:41:19Zen
dc.date.issued2022-08-27en
dc.description.abstractDetermining accurate estimates for the characteristics of the severe acute respiratory syndrome coronavirus 2 in the upper and lower respiratory tracts, by fitting mathematical models to data, is made difficult by the lack of measurements early in the infection. To determine the sensitivity of the parameter estimates to the noise in the data, we developed a novel two-patch within-host mathematical model that considered the infection of both respiratory tracts and assumed that the viral load in the lower respiratory tract decays in a density dependent manner and investigated its ability to match population level data. We proposed several approaches that can improve practical identifiability of parameters, including an optimal experimental approach, and found that availability of viral data early in the infection is of essence for improving the accuracy of the estimates. Our findings can be useful for designing interventions.en
dc.description.notesSMC acknowledges support from National Science Foundation Grants No. DMS-1813011 and DMS-2051820 and by the Virginia Tech Center for Emerging, Zoonotic, and Arthropod-borne Pathogens (CeZAP) seed Grant. NT acknowledges partial support from National Science Foundation Grant DMS-1951626.en
dc.description.sponsorshipNational Science Foundation [DMS-1951626, DMS-1813011, DMS-2051820]; Virginia Tech Center for Emerging, Zoonotic, and Arthropod-borne Pathogens (CeZAP) seed Granten
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41598-022-18683-xen
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.other14637en
dc.identifier.pmid36030320en
dc.identifier.urihttp://hdl.handle.net/10919/112163en
dc.identifier.volume12en
dc.language.isoenen
dc.publisherNature Portfolioen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleIdentifiability of parameters in mathematical models of SARS-CoV-2 infections in humansen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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