Literature DB >> 19302316

Incorporating time postinoculation into a dose-response model of Yersinia pestis in mice.

Y Huang1, T A Bartrand, C N Haas, M H Weir.   

Abstract

AIMS: To develop a time-dependent dose-response model for describing the survival of animals exposed to Yersinia pestis. METHODS AND
RESULTS: Candidate time-dependent dose-response models were fitted to a survival data set for mice intraperitoneally exposed to graded doses of Y. pestis using the maximum likelihood estimation method. An exponential dose-response model with the model parameter modified by an inverse-power dependency of time postinoculation provided a statistically adequate fit to the experimental survival data. This modified model was verified by comparison with prior studies.
CONCLUSIONS: The incorporated time dependency quantifies the expected temporal effect of in vivo bacteria growth in the dose-response relationship. The modified model describes the development of animal infectious response over time and represents observed responses accurately. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first study to incorporate time in a dose-response model for Y. pestis infection. The outcome may be used for the improved understanding of in vivo bacterial dynamics, improved postexposure decision making or as a component to better assist epidemiological investigations.

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Mesh:

Year:  2009        PMID: 19302316     DOI: 10.1111/j.1365-2672.2009.04248.x

Source DB:  PubMed          Journal:  J Appl Microbiol        ISSN: 1364-5072            Impact factor:   3.772


  5 in total

1.  Quantification of the relationship between bacterial kinetics and host response for monkeys exposed to aerosolized Francisella tularensis.

Authors:  Yin Huang; Charles N Haas
Journal:  Appl Environ Microbiol       Date:  2010-11-29       Impact factor: 4.792

2.  Modeling Rabbit Responses to Single and Multiple Aerosol Exposures of Bacillus anthracis Spores.

Authors:  Margaret E Coleman; Harry M Marks; Timothy A Bartrand; Darrell W Donahue; Stephanie A Hines; Jason E Comer; Sarah C Taft
Journal:  Risk Anal       Date:  2017-01-25       Impact factor: 4.000

3.  Development of a microbial dose response visualization and modelling application for QMRA modelers and educators.

Authors:  Mark H Weir; Jade Mitchell; William Flynn; Joanna M Pope
Journal:  Environ Model Softw       Date:  2016-11-24       Impact factor: 5.288

4.  Dose-response model of murine typhus (Rickettsia typhi): time post inoculation and host age dependency analysis.

Authors:  Sushil B Tamrakar; Yin Huang; Sondra S Teske; Charles N Haas
Journal:  BMC Infect Dis       Date:  2012-03-30       Impact factor: 3.090

5.  Dose-response time modelling for highly pathogenic avian influenza A (H5N1) virus infection.

Authors:  M Kitajima; Y Huang; T Watanabe; H Katayama; C N Haas
Journal:  Lett Appl Microbiol       Date:  2011-08-25       Impact factor: 2.858

  5 in total

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