E Y Seto | PubMed Health

PubMed1998

An analysis of the Milwaukee cryptosporidiosis outbreak based on a dynamic model of the infection process.

We combined information on the temporal pattern of disease incidence for the 1993 cryptosporidiosis outbreak in Milwaukee with information on oocyst levels to obtain insight into the epidemic process. We constructed a dynamic process model of the epidemic with continuous population compartments using reasonable ranges for the possible distribution of the model parameters. We then explored which combinations of parameters were consistent with the observations. A poor fit of the March 1-22 portion of the time series suggested that a smaller outbreak occurred before the March 23 treatment failure, beginning sometime on or before March 1. This finding suggests that had surveillance systems detected the earlier outbreak, up to 85% of the cases might have been prevented. The same conclusion was obtained independent of the model by transforming the incidence time series data of Mac Kenzie et al. This transformation is based on a background monthly incidence rate for watery diarrhea in the Milwaukee area of 0.5%. Further analysis using the incidence data from the onset of the major outbreak, March 23, through the end of April, resulted in three inferred properties of the infection process: (1) the mean incubation period was likely to have been between 3 and 7 days; (2) there was a necessary concurrent increase in Cryptospordium oocyst influent concentration and a decrease in treatment efficiency of the water; and (3) the variability of the dose-response function in the model did not appreciably affect the simulated outbreaks.

Animals↗

PubMed1996

Quantifying water pathogen risk in an epidemiological framework.

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.

Animals↗