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.
Eisenberg
J N
JN
School of Public Health, University of California, Berkeley 94720-7360, USA.
Seto
E Y
EY
Colford
J M
JM
Olivieri
A
A
Spear
R C
RC
D000818:Animals; D003198:Computer Simulation; D003457:Cryptosporidiosis / Q000453:epidemiology*; D003458:Cryptosporidium / Q000472:pathogenicity; D003967:Diarrhea / Q000382:microbiology; D004196:Disease Outbreaks; D018562:Disease Transmission, Infectious; D006801:Humans; D015994:Incidence; D008954:Models, Biological; D013997:Time Factors; D014871:Water Microbiology; D014881:Water Supply; D014922:Wisconsin