Document Detail


The structural identifiability of the susceptible infected recovered model with seasonal forcing.
MedLine Citation:
PMID:  15854675     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Neil D Evans; Lisa J White; Michael J Chapman; Keith R Godfrey; Michael J Chappell
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Mathematical biosciences     Volume:  194     ISSN:  0025-5564     ISO Abbreviation:  Math Biosci     Publication Date:  2005 Apr 
Date Detail:
Created Date:  2005-04-27     Completed Date:  2005-08-23     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0103146     Medline TA:  Math Biosci     Country:  United States    
Other Details:
Languages:  eng     Pagination:  175-97     Citation Subset:  IM    
Affiliation:
School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Communicable Diseases / epidemiology*
Epidemiologic Methods
Humans
Incidence
Models, Theoretical*
Nonlinear Dynamics
Prevalence
Seasons*

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