Abstract
Quantifying the human health risk of microbial infection helps inform regulatory policies concerning pathogens, and the associated public health measures. Estimating the infection risk requires knowledge of the probability of a person being infected by a given quantity of pathogens, and this relationship is modeled using pathogen specific dose response models (DRMs). However, risk quantification for antibiotic-resistant bacteria (ARB) has been hindered by the absence of suitable DRMs for ARB. A new approach to DRMs is introduced to capture ARB and antibiotic-susceptible bacteria (ASB) dynamics as a stochastic simple death (SD) process. By bridging SD with data from bench experiments, we demonstrate methods to (1) account for the effect of antibiotic concentrations and horizontal gene transfer on risk; (2) compute total risk for samples containing multiple bacterial types (e.g., ASB, ARB); and (3) predict if illness is treatable with antibiotics. We present a case study of exposure to a mixed population of Gentamicin-susceptible and resistant Escherichia coli and predict the health outcomes for varying Gentamicin concentrations. Thus, this research establishes a new framework to quantify the risk posed by ARB and antibiotics.
Highlights
Quantifying the human health risk of microbial infection helps inform regulatory policies concerning pathogens, and the associated public health measures
Antibiotics and other selective pressures in the environment promote enrichment of antibiotic-resistant bacteria (ARB) and induce de-novo resistance mutations in antibiotic susceptible bacteria (ASB) or the uptake of antibiotic resistant genes (ARG) which is known as horizontal gene transfer (HGT)
The initial die-off of the bacteria after they enter the host can be modeled as a stochastic death process, which is a kind of continuous time Markov chains (CTMCs)
Summary
Quantifying the human health risk of microbial infection helps inform regulatory policies concerning pathogens, and the associated public health measures. Few past studies (e.g.13,14) had investigated the burden of ARB using a top-down approach, in which the contribution of veterinary AB use to the overall number of AB resistant disease instances was investigated[12]. This top-down framework cannot be used to compute the risk posed by an exposure event (such as swimming in the recreational waters discussed in15), nor can it be used to set regulatory guidelines for acceptable levels of ARB or residual ABs in the environment. Since DRMs tailored to ARB don’t exist, these studies draw on epidemiological data (e.g. annual illness cases where some AB fails) to predict human health effects. These past studies are useful to draw inferences on the region that the data are based on but may not be applicable to other regions e.g. resource
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
