Literature DB >> 19187487

Time-dose-response models for microbial risk assessment.

Yin Huang1, Charles N Haas.   

Abstract

While microbial risk assessment (MRA) has been used for over 25 years, traditional dose-response analysis has only predicted the overall risk of adverse consequences from exposure to a given dose. An important issue for consequence assessment from bioterrorist and other microbiological exposure is the distribution of cases over time due to the initial exposure. In this study, the classical exponential and beta-Poisson dose-response models were modified to include exponential-power dependency of time post inoculation (TPI) or its simplified form, exponential-reciprocal dependency of TPI, to quantify the time of onset of an effect presumably associated with the kinetics of in vivo bacterial growth. Using the maximum likelihood estimation approach, the resulting time-dose-response models were found capable of providing statistically acceptable fits to all tested pooled animal survival dose-response data. These new models can consequently describe the development of animal infectious response over time and represent observed responses fairly accurately. This is the first study showing that a time-dose-response model can be developed for describing infections initiated by various pathogens. It provides an advanced approach for future MRA frameworks.

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Year:  2009        PMID: 19187487     DOI: 10.1111/j.1539-6924.2008.01195.x

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  14 in total

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5.  Deterministic models of inhalational anthrax in New Zealand white rabbits.

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7.  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

8.  Dose-response algorithms for water-borne Pseudomonas aeruginosa folliculitis.

Authors:  D J Roser; B Van Den Akker; S Boase; C N Haas; N J Ashbolt; S A Rice
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9.  Unveiling time in dose-response models to infer host susceptibility to pathogens.

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Review 10.  Pseudomonas aeruginosa dose response and bathing water infection.

Authors:  D J Roser; B van den Akker; S Boase; C N Haas; N J Ashbolt; S A Rice
Journal:  Epidemiol Infect       Date:  2013-11-08       Impact factor: 4.434

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