1. Technical Sessions IV. Panel discussion Applications of the data to Risk Assessment models and regulatory decision-making III. Biology of cryptosporidiosis

2. Speakers and Affiliations §Mike Arrowood (Centers for Disease Control) §Ricardo DeLeon (Metropolitan Water District of Southern California) §Terri Slifko and Joan Rose (University of South Florida) §Saul Tzipori (Tufts University) §Cynthia Chappell (University of Texas-Houston)

3. Panelists §Charles Haas (Drexel University) §Jack Colford (University of California at Berkeley-SPH) §Mary Alice Smith (University of Georgia)

4. Median Infective Dose § Dose at which 50% of test group of volunteers administered a pathogen develop adverse effects such as diarrheal illness § Abbreviation for this term is ID 50 l no subscript, confusing meaning probabilistically l no one true “infective dose” likely biologically for a population

5. Uncertainty in Dose-Response Modeling dose Response (Fraction Ill) 0.5__ 1.0__

6. Bootstrap Parameter Uncertainty infant mice (Pai, 1986) F Doses ã 10 5 ã 10 6 ã 10 7 ã 10 8 ã 10 9 ã 3x10 9 ã 10 10 F Responses 1/3 2/5 5/5 12/13 5/5 2/2 6/6 Simulate fraction ill for each dose using binomial assumption.

7. Plot Clark Carrington (FDA/CFSAN) C++ Object, no program or programming required

8. Plot Clark Carrington (FDA/CFSAN) C++ Object, no program or programming required

9. Plot Clark Carrington (FDA/CFSAN) C++ Object, no program or programming required

10. Plot Clark Carrington (FDA/CFSAN) C++ Object, no program or programming required

11. Technical Issues ¶ Are there biological models/sources of data that are not useful for modeling the human dose-response relationship?

12. Technical Issues · How useful are different biological models as a source of data for modeling human dose-response relationships?

13. Technical Issues ¸ How well do dose-response models based on data from human trials predict human dose-response relationships during outbreaks?

14. Technical Issues ¹ Are the currently available data or model systems adequate for modeling human dose-response issues? If not, what new data would be most useful?

15. Technical Issues º What lessons can we learn from Cryptosporidium dose-response modeling that can inform model systems for other pathogens?

16. Technical Issues » Can data from different biological models shed light on the basic underlying processes that risk assessors need to develop plausible mathematical models?

17. RAC Public Meeting Tuesday evening Panel Including Dale Hattis & Marion Wooldridge