Literature DB >> 23040120

Investigating effects of between- and within-host variability on Escherichia coli O157 shedding pattern and transmission.

S Chen1, M Sanderson, C Lanzas.   

Abstract

Healthy cattle and their environment are the reservoir for the human pathogen Escherichia coli O157. In E. coli O157 epidemiology, supershedders have been loosely defined as cattle that shed high concentrations of E. coli O157 (≥ 10(4)colony-forming cells (CFU)/g of feces) at a single (or multiple) cross-section in time. Due to the variability in the pathogen shedding level among animals (between-host variability), as well as fluctuations in the level shed by a single animal (within-host variability), it is difficult to interpret fecal bacteria distributions, as well as to parse the relative contribution of between- and within-host variability to the observed shedding patterns at the pen level. We developed an agent-based model that integrates individual animal data on temporal fecal shedding dynamics with pen-level E. coli O157 transmission to study how the temporal (and aggregation) patterns of E. coli O157 shedding loads and prevalence arise at the pen level. We demonstrated that even without between-host variability, the prevalence of animals with concentration of E. coli O157 in feces that exceeds 10(4)CFU/g is similar to that observed in cross-sectional field data. Both within-host and between-host variability can generate supershedders.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 23040120     DOI: 10.1016/j.prevetmed.2012.09.012

Source DB:  PubMed          Journal:  Prev Vet Med        ISSN: 0167-5877            Impact factor:   2.670


  5 in total

1.  Basic Reproduction Number and Transmission Dynamics of Common Serogroups of Enterohemorrhagic Escherichia coli.

Authors:  Shi Chen; Michael W Sanderson; Chihoon Lee; Natalia Cernicchiaro; David G Renter; Cristina Lanzas
Journal:  Appl Environ Microbiol       Date:  2016-08-30       Impact factor: 4.792

2.  Temporal-spatial heterogeneity in animal-environment contact: implications for the exposure and transmission of pathogens.

Authors:  Shi Chen; Michael W Sanderson; Brad J White; David E Amrine; Cristina Lanzas
Journal:  Sci Rep       Date:  2013-11-01       Impact factor: 4.379

3.  Transmission on empirical dynamic contact networks is influenced by data processing decisions.

Authors:  Daniel E Dawson; Trevor S Farthing; Michael W Sanderson; Cristina Lanzas
Journal:  Epidemics       Date:  2018-08-29       Impact factor: 4.396

4.  Highly dynamic animal contact network and implications on disease transmission.

Authors:  Shi Chen; Brad J White; Michael W Sanderson; David E Amrine; Amiyaal Ilany; Cristina Lanzas
Journal:  Sci Rep       Date:  2014-03-26       Impact factor: 4.379

5.  Investigating behavioral drivers of seasonal Shiga-Toxigenic Escherichia Coli (STEC) patterns in grazing cattle using an agent-based model.

Authors:  Daniel E Dawson; Jocelyn H Keung; Monica G Napoles; Michael R Vella; Shi Chen; Michael W Sanderson; Cristina Lanzas
Journal:  PLoS One       Date:  2018-10-10       Impact factor: 3.240

  5 in total

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