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Mathematical models for assessment of human health risk of pathogens in the environment

  • Author(s): Chandrasekaran, Srikiran
  • Advisor(s): Jiang, Sunny C
  • et al.
Creative Commons 'BY' version 4.0 license
Abstract

Microbial pathogens in the environment present a growing threat to human health. They are found in waters used for recreation, irrigation etc., which present a multitude of pathways in which a person may be exposed to them. Moreover, the increasing interest in reusing treated wastewater raises questions of water quality and associated public health risks. The overarching goal of my dissertation is to develop quantitative tools to improve the accuracy of methods used to assess this risk and provide insights into disease development. I hypothesize that mathematical models rooted in well-grounded theory and data can augment the overall understanding of microbial risk and steer experiments in the most productive direction. My specific aims are: 1) To quantify the health risk posed by norovirus-contaminated water used for lettuce irrigation, by means of a dynamic transport model 2) To develop a dose-response framework applicable for antibiotic resistant bacteria, specifically the human enteric pathogen, Escherichia coli 3) To develop a modeling framework to probe the importance of cooperativity in helping Staphylococcus aureus establish skin infections. In my research, I have constructed a transport model using ordinary differential equations to predict the norovirus load in lettuce at harvest given the load in the irrigation water. By fitting this model to published experimental data, I found that attachment of the virus to the growth medium strongly influences the amount of virus in lettuce at harvest. Towards the second aim, I have used stochastic processes to develop an analytical expression for E. coli dose-response. I then fitted this to clinical data and extended the model to predict, for the first time, the risk posed by a mixture of antibiotic sensitive and antibiotic resistant strains. Towards the third aim, I have developed a two-compartment stochastic model with cooperativity between cells to predict S. aureus dose-response. Using experimental data to reject the hypothesis of absence of cooperativity, I show the possible role of quorum sensing in S. aureus establishing skin infections. The outcomes of this research will enable better understanding of microbial risk associated with environmental exposure and improve human health protection.

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