Document Detail

Epidemiological models with non-exponentially distributed disease stages and applications to disease control.
MedLine Citation:
PMID:  17237913     Owner:  NLM     Status:  MEDLINE    
SEIR epidemiological models with the inclusion of quarantine and isolation are used to study the control and intervention of infectious diseases. A simple ordinary differential equation (ODE) model that assumes exponential distribution for the latent and infectious stages is shown to be inadequate for assessing disease control strategies. By assuming arbitrarily distributed disease stages, a general integral equation model is developed, of which the simple ODE model is a special case. Analysis of the general model shows that the qualitative disease dynamics are determined by the reproductive number [Formula: see text], which is a function of control measures. The integral equation model is shown to reduce to an ODE model when the disease stages are assumed to have a gamma distribution, which is more realistic than the exponential distribution. Outcomes of these models are compared regarding the effectiveness of various intervention policies. Numerical simulations suggest that models that assume exponential and non-exponential stage distribution assumptions can produce inconsistent predictions.
Zhilan Feng; Dashun Xu; Haiyun Zhao
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2007-01-20
Journal Detail:
Title:  Bulletin of mathematical biology     Volume:  69     ISSN:  0092-8240     ISO Abbreviation:  Bull. Math. Biol.     Publication Date:  2007 Jul 
Date Detail:
Created Date:  2007-06-19     Completed Date:  2007-10-18     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0401404     Medline TA:  Bull Math Biol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1511-36     Citation Subset:  IM    
Department of Mathematics, Purdue University, West Lafayette, IN 47907, USA.
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MeSH Terms
Communicable Diseases / epidemiology*,  transmission
Computer Simulation
Infection Control / methods*
Models, Biological*
Patient Isolation
Statistical Distributions

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