Mechanistic Insights into Dynamic Predictions of Pathogens in Engineered Systems

Pathogens can proliferate in the built environment and can cause disease outbreaks if water and wastewater are not properly managed. Understanding pathogens that grow in engineered systems is crucial to protecting public health and preventing disease. Using dynamic computational models can reveal mechanistic insights into these systems to aid in understanding risk drivers and determining risk management strategies. The first research chapter of this thesis investigates tradeoffs for reducing the cost associated with Legionnaire’s Disease, hot water scalding, and energy use using a computational framework for evaluating an optimal water heater temperature set point. The model demonstrated that the optimal temperature set point was highly dependent on assumptions made regarding the dose response parameter for a common configuration of an electric water heater in a hospital setting. The optimal temperature was 55°C or 48°C for subclinical vs. clinical severity dose response, respectively, compared with current recommendations of 60°C to kill bacteria and 49°C to prevent scalding and conserve energy. The second research chapter models the population dynamics of antibiotic-susceptible Escherichia coli (E. coli) and antibiotic-resistant E. coli with a population ecology-exposure assessment model in surface water to quantify the risk of urinary tract infection from recreational swimming activities. Horizontal gene transfer (HGT) was modeled in the environment and the human gastrointestinal tract for several scenarios. HGT was generally not a dominant driver of exposure estimates compared to other factors such as growth and dilution, however, the rank order of factors was scenario-dependent. The final research chapter models pathogen transport from wastewater treatment plant (WWTP) exposures and assesses the risk to workers based on several exposure scenarios. Case studies were performed to investigate infection risk drivers across different scenarios, including adjustments for the timing of exposure and personal protective equipment. A web application was developed for use by WWTP risk managers to be used with site-specific data. The proposed modeling frameworks identified risk drivers across several microbial risk scenarios and provide flexible tools for risk managers to use when making water treatment and use decisions for water management plans used for premise plumbing as well as for wastewater treatment practices.