No 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.

Food production using recycled wastewater offers a sustainable way forward in light of limited freshwater resources. However, concerns of food safety should be addressed to protect public health. To this end, we developed a dynamic transport model to track norovirus from the irrigation water to the root and shoot of lettuce during the growth period. These processes were embodied in a system of ordinary differential equations that also incorporated plant growth, transpiration rate, viral attachment and detachment to culture media, viral decay, and plant barrier effects. Model parameters were either obtained from the literature or through fitting the model to experimental data from a study reporting human norovirus transport in hydroponically grown lettuce. The results showed that lettuce grown hydroponically resulted in a higher risk than lettuce grown in soil. In both cases, the risk predicted failed to meet the risk benchmarks established by the U.S. EPA and WHO. Viral attachment to growth media, such as the soil particles, was an important mechanism for risk reduction. A sensitivity analysis revealed that harvesting time and irrigation time are important factors influencing the viral loads in lettuce. Hence, this pathogen transport model provides a framework for investigating the effects of time and other factors on disease burdens from water reuse in agriculture, underscoring the utility of a dynamic model. In the absence of a routine monitoring of contaminants in the recycled irrigation water and food crops, a quantitative risk assessment based on objective scientific knowledge is the best approach to guide the policy decisions on water reuse practices.