Quantifying Water Pathogen Risk in an Epidemiological Framework

Abstract

Traditionally, microbial risk assessors have used point estimates to evaluate the probability that an individual will become infected. We developed a quantitative approach that shifts the risk characterization perspective from point estimate to distributional estimate, and from individual to population. To this end, we first designed and implemented a dynamic model that tracks traditional epidemiological variables such as the number of susceptible, infected, diseased, and immune, and environmental variables such as pathogen density. Second, we used a simulation methodology that explicitly acknowledges the uncertainty and variability associated with the data. Specifically, the approach consists of assigning probability distributions to each parameter, sampling from these distributions for Monte Carlo simulations, and using a binary classification to assess the output of each simulation. A case study is presented that explores the uncertainties in assessing the risk of giardiasis when swimming in a recreational impoundment using reclaimed water. Using literature-based information to assign parameters ranges, our analysis demonstrated that the parameter describing the shedding of pathogens by infected swimmers was the factor that contributed most to the uncertainty in risk. The importance of other parameters was dependent on reducing the a priori range of this shedding parameter. By constraining the shedding parameter to its lower subrange, treatment efficiency was the parameter most important in predicting whether a simulation resulted in prevalences above or below non outbreak levels. Whereas parameters associated with human exposure were important when the shedding parameter was constrained to a higher subrange. This Monte Carlo simulation technique identified conditions in which outbreaks and/or nonoutbreaks are likely and identified the parameters that most contributed to the uncertainty associated with a risk prediction.

Strategies to Reduce Person-to-Person Transmission during WidespreadEscherichia coliO157:H7 Outbreak

“…An existing epidemiologic model for disease transmission (11,12) was adapted to simulate the 2006 US E. coli O157 outbreak. The most recent data from CDC (13) were used, along with data from the published literature, to replicate the E. coli O157:H7 prevalence estimated by CDC in the United States and the reported outbreak conditions as of September 19, 2006 (13), and then to evaluate the potential effect of the timing and effectiveness of interventions on secondary infections.…”

Disease transmission models for public health decision making: analysis of epidemic and endemic conditions caused by waterborne pathogens.

“…Surprisingly, despite the increase in the use of disease transmission models to study infectious disease processes and despite the known, massive public health problems associated with infectious diarrhea and gastrointestinal illness worldwide, few publications have examined disease transmission through waterborne pathogens. Two of these publications, by Eisenberg et al (8) and Brookhart et al (9), demonstrate the importance of both disease transmission and the immune process in understanding risk, and are discussed below.…”

“…An additional parameter in the model, ρ, represents the proportion of asymptomatic infections. For more mathematical detail pertaining to the model, see previous publications (8,9).…”

“…We then analyzed the multivariate parameter distribution associated with a background/above background classification using the classification and regression tree (CART) algorithm, which builds a tree based on minimizing the classification error (29). The details of this approach are given in a previous publication (8).…”

“…To illustrate the use of transmission models in risk assessment, Eisenberg et al (8) developed an exposure scenario in which swimmers were exposed to Giardia from a recreational swimming impoundment filled with water reclaimed from community sewage. Because they had no incidence data from which to evaluate the model, the focus of that study was to compare two conditions: one in which the swimming impoundment water was not the exposure vehicle and one in which water was an exposure vehicle.…”