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dc.contributor.authorChilds, Lauren M.en
dc.contributor.authorEl Moustaid, Fadouaen
dc.contributor.authorGajewski, Zacharyen
dc.contributor.authorKadelka, Sarahen
dc.contributor.authorNikin-Beers, Ryanen
dc.contributor.authorSmith, John W., Jr.en
dc.contributor.authorWalker, Melodyen
dc.contributor.authorJohnson, Leah R.en
dc.date.accessioned2019-07-24T17:26:15Zen
dc.date.available2019-07-24T17:26:15Zen
dc.date.issued2019-06-19en
dc.identifier.issn2167-8359en
dc.identifier.othere7057en
dc.identifier.urihttp://hdl.handle.net/10919/91974en
dc.description.abstractThe observed dynamics of infectious diseases are driven by processes across multiple scales. Here we focus on two: within-host, that is, how an infection progresses inside a single individual (for instance viral and immune dynamics), and between-host, that is, how the infection is transmitted between multiple individuals of a host population. The dynamics of each of these may be influenced by the other, particularly across evolutionary time. Thus understanding each of these scales, and the links between them, is necessary for a holistic understanding of the spread of infectious diseases. One approach to combining these scales is through mathematical modeling. We conducted a systematic review of the published literature on multi-scale mathematical models of disease transmission (as defined by combining within-host and between-host scales) to determine the extent to which mathematical models are being used to understand across-scale transmission, and the extent to which these models are being confronted with data. Following the PRISMA guidelines for systematic reviews, we identified 24 of 197 qualifying papers across 30 years that include both linked models at the within and between host scales and that used data to parameterize/calibrate models. We find that the approach that incorporates both modeling with data is under-utilized, if increasing. This highlights the need for better communication and collaboration between modelers and empiricists to build well-calibrated models that both improve understanding and may be used for prediction.en
dc.language.isoenen
dc.publisherPeerJen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectMulit-scale modelingen
dc.subjectLinking mechanismen
dc.subjectInfectious disease modelsen
dc.subjectSIR modelsen
dc.subjectData-model integrationen
dc.subjectWithin-hosten
dc.subjectBetween-hosten
dc.subjectPathogen transmissionen
dc.titleLinked within-host and between-host models and data for infectious diseases: a systematic reviewen
dc.typeArticle - Refereeden
dc.contributor.departmentBiological Sciencesen
dc.contributor.departmentMathematicsen
dc.contributor.departmentStatisticsen
dc.title.serialPeerJen
dc.identifier.doihttps://doi.org/10.7717/peerj.7057en
dc.identifier.volume7en
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen
dc.identifier.pmid31249734en


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International