Community Level Impacts of Waterborne Infection. C. N. Haas and M. Gupta, Drexel University, Philadelphia PA
To date, microbial risk assessment for pathogens has focused on estimating attack proportions. Outbreaks of illness may result in non-linear costs (e.g., the impact of 1000 illnesses clustered in time may be substantially greater than 1000 illnesses separated in time). To assess the potential for such clusters, dynamic models of the disease transmission process must be formulated. Prior formulations have used rudimentary formulations for incubation time and disease latency. In this work, we report fitting a data set of Cryptosporidium infection (the 1993 Milwaukee outbreak). This fitting results in quantitative estimates for the incubation time distribution (the time between exposure and frank illness) and for the instantaneous risk of infection on each day of exposure. The incubation time distribution can then be applied to different scenarios. We first ask the question of what impact earlier intervention might have had in reducing illnesses in the Milwaukee outbreak. The use of the derived parameters for assessing potential future events will be discussed.
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