title: Cryptosporidium Infection Risk: Results of New Dose-Response Modeling.
abstract: Cryptosporidium human dose-response data from seven species/isolates are used to investigate six models of varying complexity that estimate infection probability as a function of dose. Previous models attempt to explicitly account for virulence differences among C. parvum isolates, using three or six species/isolates. Four (two new) models assume species/isolate differences are insignificant and three of these (all but exponential) allow for variable human susceptibility. These three human-focused models (fractional Poisson, exponential with immunity and beta-Poisson) are relatively simple yet fit the data significantly better than the more complex isolate-focused models. Among these three, the one-parameter fractional Poisson model is the simplest but assumes that all Cryptosporidium oocysts used in the studies were capable of initiating infection. The exponential with immunity model does not require such an assumption and includes the fractional Poisson as a special case. The fractional Poisson model is an upper bound of the exponential with immunity model and applies when all oocysts are capable of initiating infection. The beta Poisson model does not allow an immune human subpopulation; thus infection probability approaches 100% as dose becomes huge. All three of these models predict significantly (>10x) greater risk at the low doses that consumers might receive if exposed through drinking water or other environmental exposure (e.g., 72% vs. 4% infection probability for a one oocyst dose) than previously predicted. This new insight into Cryptosporidium risk suggests additional inactivation and removal via treatment may be needed to meet any specified risk target, such as a suggested 10-4annual risk of Cryptosporidium infection.
authors.affiliation: Office of Water, U.S. Environmental Protection Agency, Washington, DC, USA.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Messner
authors.affiliation: Office of Water, U.S. Environmental Protection Agency, Washington, DC, USA.
authors.forename: Philip
authors.initials: P
authors.lastname: Berger
mesh_terms: D001499:Bayes Theorem; D003457:Cryptosporidiosis / Q000175:diagnosis*; D003458:Cryptosporidium; D004198:Disease Susceptibility; D006801:Humans; D007107:Immune System; D008962:Models, Theoretical; D033661:Oocysts; D016012:Poisson Distribution; D011336:Probability; D018570:Risk Assessment / Q000379:methods*; D014774:Virulence
keywords: Beta-Poisson; Cryptosporidium; dose-response; exponential with immunity; fractional Poisson