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

Abstract

Understanding the transmission dynamics of pathogens is essential to determine the epidemiology, ecology, and ways of controlling enterohemorrhagic Escherichia coli (EHEC) in animals and their environments. Our objective was to estimate the epidemiological fitness of common EHEC strains in cattle populations. For that purpose, we developed a Markov chain model to characterize the dynamics of 7 serogroups of enterohemorrhagic Escherichia coli (O26, O45, O103, O111, O121, O145, and O157) in cattle production environments based on a set of cross-sectional data on infection prevalence in 2 years in two U.S. states. The basic reproduction number (R0) was estimated using a Bayesian framework for each serogroup based on two criteria (using serogroup alone [the O-group data] and using O serogroup, Shiga toxin gene[s], and intimin [eae] gene together [the EHEC data]). In addition, correlations between external covariates (e.g., location, ambient temperature, dietary, and probiotic usage) and prevalence/R0 were quantified. R0 estimates varied substantially among different EHEC serogroups, with EHEC O157 having an R0 of >1 (∼1.5) and all six other EHEC serogroups having an R0 of less than 1. Using the O-group data substantially increased R0 estimates for the O26, O45, and O103 serogroups (R0 > 1) but not for the others. Different covariates had distinct influences on different serogroups: the coefficients for each covariate were different among serogroups. Our modeling and analysis of this system can be readily expanded to other pathogen systems in order to estimate the pathogen and external factors that influence spread of infectious agents.

In this paper we describe a Bayesian modeling framework to estimate basic reproduction numbers of multiple serotypes of Shiga toxin-producing Escherichia coli according to a cross-sectional study. We then coupled a compartmental model to reconstruct the infection dynamics of these serotypes and quantify their risk in the population. We incorporated different sensitivity levels of detecting different serotypes and evaluated their potential influence on the estimation of basic reproduction numbers.

MIDAS Network Members

Shi Chen

Assistant Professor
University of North Carolina at Charlotte

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