| The structural identifiability of the susceptible infected recovered model with seasonal forcing. | |
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MedLine Citation:
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PMID: 15854675 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In this paper, it is shown that the SIR epidemic model, with the force of infection subject to seasonal variation, and a proportion of either the prevalence or the incidence measured, is unidentifiable unless certain key system parameters are known, or measurable. This means that an uncountable number of different parameter vectors can, theoretically, give rise to the same idealised output data. Any subsequent parameter estimation from real data must be viewed with little confidence as a result. The approach adopted for the structural identifiability analysis utilises the existence of an infinitely differentiable transformation that connects the state trajectories corresponding to parameter vectors that give rise to identical output data. When this approach proves computationally intractable, it is possible to use the converse idea that the existence of a coordinate transformation between states for particular parameter vectors implies indistinguishability between these vectors from the corresponding model outputs. |
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Authors:
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Neil D Evans; Lisa J White; Michael J Chapman; Keith R Godfrey; Michael J Chappell |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Mathematical biosciences Volume: 194 ISSN: 0025-5564 ISO Abbreviation: Math Biosci Publication Date: 2005 Apr |
Date Detail:
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Created Date: 2005-04-27 Completed Date: 2005-08-23 Revised Date: 2009-11-11 |
Medline Journal Info:
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Nlm Unique ID: 0103146 Medline TA: Math Biosci Country: United States |
Other Details:
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Languages: eng Pagination: 175-97 Citation Subset: IM |
Affiliation:
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School of Engineering, University of Warwick, Coventry CV4 7AL, UK. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Communicable Diseases / epidemiology* Epidemiologic Methods Humans Incidence Models, Theoretical* Nonlinear Dynamics Prevalence Seasons* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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