Microbial Risk Assessment

Research Areas


Exposure Assessment

Dose–Response Modeling

Population Outcomes

Risk Characterization and Communication

Knowledge Management


About Us

Michigan State University
301 Manly Miles Building
East Lansing, MI 48824
Tel: 517-355-1655
Website:  www.camra.msu.edu

Leadership:

Dr. Joan B. Rose, Co-Director
Dr. Charles N. Haas, Co-Director

Project Search

Center for Advancing Microbial Risk Assessment

The Center for Advancing Microbial Risk Assessment (CAMRA) is a multidisciplinary Center of Excellence funded by the Department of Homeland Security and the Environmental Protection Agency. The Center focuses research towards preparing and providing the best tools for decision and policy makers to mitigate microbial hazards. This work focuses on potential bioterror agents and other infectious pathogens using the quantitative microbial risk assessment (QMRA) framework. Expanding both the QMRA paradigm and solidifying it as an independent science, as well as extending the QMRA community, increases the reach and capabilities of CAMRA now and in the future.

Center Activity

Project Spotlights

New QMRAwiki

CAMRA is developing a central repository in a wiki format, to make quantitative microbial risk assessment information available and accessible to the public. The wiki will provide a reliable source of information on microbiological hazards with data, information, and new developments included in the content.  In the future the site will become more interactive and feature a strong tutorial for users to learn QMRA. This site is a valuable source of information for those new to the field of QMRA or for teachers who are looking for new material. The wiki will contribute to building a network of individuals knowledgeable in the field of quantitative microbial risk assessment. The new QMRAwiki is up, and can be accessed here.

Current Projects

Project I - Exposure: Detection, Fate and Transport of Biological Agents of Concern (BAC)
Team members have used metagenomics approaches to address fomite contamination and hazard discovery for viruses finding that a wide array of bacteriophage are likely targets for further work to improve exposure assessment. Evaluation and characterization of bacterial samples collected from the fomite surfaces in dormitories at the University of Michigan by sequencing 16S rRNA genes using 454 FLX sequencing technology. Primers designed to target conserved regions surrounding hyper-variable regions of relevant genes will be used to amplify the 16S rRNA sequences and sequenced by high throughput sequencing. Signature sequences found with 454 Sequencing Technology will be evaluated for the presence of pathogenic and non-pathogenic organisms on the fomites. 

For drinking water pathways Project I showed the usefulness of a risk assessment simulation as a way of evaluating sensor placement and tested the axial dispersion of a sodium chloride tracer passing through a cross junction to evaluate AZRED-II in comparison to both EPANET and AZRED-I and embedded axial dispersion into the AZRED code in order to fully integrate both of the improved transport assumptions for water quality analyses. 

Project II - Infectious Disease Models for Assessing Microbial Risks for Developing Control Strategies
Used a deterministic differential equation based model to describe the hand and environmental mediated transmission of Methicillin-resistant Staphylococcus aureus (MRSA). Project II conducted research on modeling influenza transmission on a college campus. Local weather variables were found to be statistically significant in association with the proportion of cases that tested positive for influenza virus, and the proportion of diagnosis in outpatients visiting Hong Kong influenza surveillance sites. Project II examined the successes and shortcomings of polio eradication using a transmission modeling analysis. 

Project III - Dose-response Modeling and Applications
Added to the impressive dose-response models developing models for Brucella species Leptospira bacteria, Rickettsia rickettsii Rickettsia typhi and Avian Influenza A (H5N1) Virus. Project III has developed the first inclusion of time post inoculation into dose response models which allows for great advancements in understanding how the body reacts to pathogen exposure and paves the way for understanding multiple dosing of pathogens effect on the host. Dose Response Models incorporating Aerosol Size Dependency were developed for F. tularensis and B. anthracis. They have also addressed Francisella tularensis associated with ingestion and multiple exposures. Human health risks posed by Aspergillus fumigatus and risk mode; for inhaled toxins associated with spores of Stachy are in progress. 

Project IV - Assessment-Analysis Interface for QMRA 
work on surface concentration standards for non-persistent pathogens has been a collaborative effort with Project III. Project III identified suitable dose response models and uncertainties for the effort. In addition, a review paper on persistence of Category A pathogens developed by Project I has been a key resource for this study. A joint paper with Project III has been published in PLOS One and received favorable reviewer comments. Project IV researchers also performed an analysis of microbial spore recovery. The use of the wipe collection method on non-porous surfaces resulted in the highest recoveries. 

Project V - Knowledge Management, Learning and Discovery for the QMRA Community
Investigated and implemented effective and efficient methods to enhance the understanding of microbial risk assessment (MRA) as a body of knowledge. Project V has built and maintained online collaborative repository and collaborated with other projects working on an open repository. A major effort has been focused on development of the CAMRA Risk Wiki. 

Recent Publications

CAMRA has published or has in press 54 papers to date with 434 citations. (citations are shown in []) Listed by each lead investigator. 

Rose 

1. Masago, Y., T. Shibata, and J. B. Rose. (2008). Bacteriophage P22 and Staphylococcus aureus Attenuation on Nonporous Fomites as Determined by Plate Assay and Quantitative PCR. Applied and Environmental Microbiology, 74(18):5838-5840. [3] 

2. Jones, R.M., Y. Masago, T. Bartrand, C.N. Haas, M. Nicas, and J.B. Rose. (2009) Characterizing the risk of infection from Mycobacterium tuberculosis in commercial passenger aircraft using quantitative microbial risk assessment, Risk Analysis. 29(3):355-365. [12] 

3. Razzolini, M.T.P. M.H. Weir. M.H. Matte, G.R. Matte, L.N. Fernandes and J.B. Rose. (2011). Risk of Giardia infection for drinking water and bathing in a peri-urban area in St. Paulo, Brazil. International Journal of Environmental Health Research, 21(3), 222-234.[0] 

4. Weir, M.H.; M.T.P. Razzolini, Y. Masago, and J.B. Rose. (2011). Water Reclamation Redesign for Reducing Cryptosporidium Risks at a Recreational Spray Park using Stochastic Models. Water Research, 45(19):6504-6514[0] 

 

Project I 

Gerba 

5. Boone, S. A. and C. P. Gerba. (2007). The significance of fomites in the spread of respiratory and gastrointestinal disease. Applied and Environmental Microbiology, 73:1687-1696. [83] 

6. Kim, M., C. Y. Choi, and C. P. Gerba.(2008). Source Tracking of Microbial Intrusion in Water Systems Using Artificial Neural Networks, Water Research, 42(4-5):1308-1314. [16] 

7. Sinclair, R., S. A. Boone, D. Greenberg, P. Keim, and C. P. Gerba. ( 2008). Persistence of Category A select agents in the environment. Applied Environmental Microbiology, 74:555-563. [42] 

8. Ryan, G, G. Sinclair, C. Y. Choi, M. R. Riley and C. P. Gerba. (2009). Pathogen surveillance through monitoring of sewer systems. Advanced Applied Microbiology, 65:249-269. [12] 

9. Sinclair, R., P.R. Gomez, C.Y. Choi, C.P. Gerba. (2009) Assessment of MS-2 phage and salt tracers to characterize axial dispersion in water distribution systems. Journal of Environmental Science and Health,44: 963-971. [1] 

10. Sinclair, R., J. B. Rose, S. A. Hashsham, C. P. Gerba and C. N. Haas. (2012). Selection of microbial surrogates for studying the fate and control of pathogens in the environment. Applied Environmental. Microbiology, 78:1969-1977 [2] 

11. Ahamd, F., S. K. Pandey, A. B. Herzog, J. B. Rose, C.. P. Gerba and S. A. Hashsham. (2012). Environmental applications and potential health implications of quantum dots. Journal of Nanoparticle Research, 14:1038 

12. Herzog, A.B., A. K. Pandey, D. Reyes-Gasteul, C. P. Gerba, J. B. Rose and S. A Hashsham. 2012. Evaluation of sample recovery for bacteriophage P22 on fomites. Applied Environmental. Microbiology, 78:7915-7922. 

 

Choi 

13. Romero-Gomez, P., C. K. Ho, and C. Y. Choi. (2008). Mixing at Cross Junctions in Water Distribution Systems – Part I. A Numerical Study. ASCE Journal of Water Resources Planning and Management, 134(3):284-294. [18] 

14. Austin, R. G., B. van Bloemen Waanders, S. McKenna and C. Y. Choi. (2008). Mixing at Cross Junctions in Water Distribution Systems – Part II. An Experimental Study. ASCE Journal of Water Resources Planning and Management. 134(3):295-302. [22] 

15. Song, I.H., P. Romero-Gomez, and C. Y. Choi. (2009). Experimental Verification of Incomplete Solute Mixing in a Pressurized Pipe Network with Multiple Cross Junctions, ASCE Journal of Hydraulic Engineering, 135:11, 1005-1011[5] 

16. Yoon, J.-Y., J.-H. Han, C. Y. Choi, M. Bui, and R. Sinclair. (2009). Real-Time Detection of Escherichia coli in Water Pipe Using a Microfluidic Device with One-Step Latex Immunoagglutination Assay, Transactions of the ASABE. 52(3): 1031-1039. [5] 

17. Romero-Gomez, P., K. E. Lansey, and C. Y. Choi. (2011). Impact of an incomplete solute mixing model on sensor network design, Journal of Hydroinformatics, 13(4):642.651. 

18. Romero-Gomez, P. and C. Y. Choi. (2011). Axial Dispersion Coefficients in Laminar Flows of Water Distribution Systems. ASCE Journal of Hydraulic Engineering , 137(11):1500-1508.[3] 

 

Hashsham 

19. Herzog, A.B., S.D. McLennan, A.K. Pandey, C.P. Gerba, C.N. Haas, J.B. Rose, and S.A. Hashsham. (2009). Implications of Limits of Detection of Various Methods for Bacillus anthracis in Computing Risk to Human Health. Applied Environmental Microbiology. 75:6331-6339. [16] 

 

Nicas 

20. Jones, R. and M. Nicas. (2009). Experimental Determination of supermicrometer particle fate subsequent to a point release within a room under natural and forced mixing. Aerosol Science and Technology, 43: 921-938. [2] 

 

Wagner 

21. Greenberg, D. L., J. D. Busch, D. M. Wagner and P. Keim. (2010). Identifying experimental surrogates for Bacillus anthracis spores: a review. Investigative Genetics, 1:4.[12] 

 

Project II 

Eisenberg 

22. Pujol, J.M., J.N. Eisenberg, C.N. Haas, and J.S. Koopman. (2009). The Effect of Ongoing Exposure Dynamics in Dose Response Relationships. PLoS Computational Biology, 5(6): e1000399. doi:10.1371/journal.pcbi.1000399. [14] 

23. Mayer, B.T., J. S. Koopman, E. L. Ionides, J. M. Pujol, and J. N. Eisenberg. (2011). A dynamic dose–response model to account for exposure patterns in risk assessment: case study in inhalation anthrax. Journal of Royal Society Interface, 8:57 506-517 [0] 

24. Li, S., J.N. Eisenberg, I. Spicknall, and J.S. Koopman. (2009). Dynamics and Control of Infections Transmitted from Person to Person Through the Environment. American Journal of Epidemiology, 170 (2): 257-265. [22] 

25. Spicknall, I., J.S. Koopman, M. Nicas, J. Pujol, L. Sheng and J.N. Eisenberg. (2010). Informing Optimal Environmental Influenza Interventions: How the Host, Agent, and Environment Alter Dominant Routes of Transmission. PLoS Computational Biology, 6(10): e1000969.[3] 

26. Zelner, J., A. A. King, C. L. Moe and J. N. Eisenberg. (2010). How Infections Propagate After Point Source Outbreaks: An Analysis of Secondary Norovirus Transmission. Epidemiology, 21(5): 711-718.[3] 

 

Project III 

Haas: 

27. Bartrand, T. A., M. H. Weir, and C. N. Haas. (2008). Dose-Response Models for Inhalation of Bacillus anthracis Spores: Interspecies Comparisons. Risk Analysis, 28(4):1115-1124. [27] 

28. Tamrakar, S.B. and C. N. Haas. (2008). Dose-Response Model for Lassa Virus. Human and Ecological Risk Assessment, 14(4): 742-752. [2] 

29. Tamrakar, S.B. and C. N. Haas. (2008). Dose-Response Model for Burkholderia pseudomallei (melioidosis). Journal of Applied Microbiology, 105(5):1361-1371. [3] 

30. Weir, M. H. and C. N. Haas. Quantification of the Effects of Age on the Dose Response of Variola major in Suckling Mice. Human and Ecological Risk Assessment, 15(6):1245:1256 [1] 

31. Huang, Y., T.A. Bartrand, C.N. Haas, and M.H. Weir. (2009). Incorporating Time Post Inoculation into a Dose-Response Model of Yersinia pestis in Mice. Journal of Applied Microbiology. 107(3):727-735. [9] 

32. Huang, Y. and C.N. Haas, (2009) Time-dose-response Models for Microbial Risk Assessment. Risk Analysis, 29(5): 648-661. [15] 

33. Huang, Y. and C. N. Haas (2011). Quantification of the relationship between bacterial kinetics and host response for monkeys exposed to aerosolized Francisella tularensisApplied and Environmental Microbiology, 77 (2): 485-490. [1] 

34. Huang, Y., T. Hong, T. A. Bartrand, P. L. Gurian, C. N. Haas, R. Liu and S. B. Tamrakar. (2010). How Sensitive Is Safe? Risk-Based Targets for Ambient Monitoring of Pathogens. IEEE Sensors Journal,10(3): 668-673.[8] 

35. Tamrakar, S. B., A. Haluska, C. N. Haas and T. A. Bartrand. (2011). Dose-Response Model of Coxiella burnetii (Q Fever). Risk Analysis, 31(1): 120-128. [4] 

36. Tamrakar S.B. and C.N. Haas. (2011). Dose-Response Model for Rocky Mountain Spotted Fever (RMSF) for Human. Risk Analysis, 31(10): 1610-1621.[3] 

37. Teske S.S., Y. Huang, T.A. Bartrand, S.B. Tamrakar, M.H. Weir, and C.N. Haas. (2011). Animal and Human Dose Response Models for Brucella species. Risk Analysis, 31(10):1576-96. [2] 

38. Kitajima, M., Y. Huang, T.Watanabe, H. Katayama and C.N. Haas. (2011). Dose-Response Time Modeling for Highly Pathogenic Avian Influenza A (H5N1) Virus Infection. Letters in Applied Microbiology, 53(4): 438–444. [1] 

39. Tamrakar, S.B., Y. Hunag, and C.N. Haas. (2012). Dose-Response Model for Murine Typhus (Rickettsia typhi): Time Post Inoculation and Host Age Dependency Analysis. BMC Infectious Disease, 12:77. 

 

Project IV 

Gurian: 

40. Corella-Barud, V., K.D. Mena, S.G. Gibbs, P.L. Gurian, and A. Barud. (2009). Evaluation of Neighborhood Treatment Systems for Potable Water Supply. International Journal of Environmental Health Research, 19(1):49-58. [2] 

41. Hong, T., P. L. Gurian and N. Ward. (2010). Setting Risk-Informed Environmental Standards for Bacillus Anthracis Spores. Risk Analysis, 30(10): 1602-1622.[9] 

42. Mitchell-Blackwood, J., P. L. Gurian and C. O’Donnell. (2011). Finding Risk-based Switchover Points for Response Decisions for Environmental Exposure to Bacillus anthracisHuman and Ecological Risk Assessment, 17(2): 489-509. [4] 

43. Solon, I., P.L. Gurian, H. Perez. (2012). The Extraction of a Bacillus anthracis Surrogate from Pleated HVAC Filter Samples. Indoor and Built Environment, 21(4): 562-567. [1] 

44. Hong, T. and P.L. Gurian. (2012). Characterizing Bioaerosol Risk from Environmental Sampling. Environmental Science and Technology, 46(12):6714-6722. 

45. Hong, T., P.L. Gurian, Y. Huang, and C.N. Haas. (2012). Prioritizing Risks and Uncertainties from Intentional Release of Selected Category A Pathogens. PLoS ONE, 7(3):e32732.[2] 

46. Mitchell-Blackwood, J., P. Gurian, R. Lee, and B.Thran. (2012). Variance in Bacillus anthracis Virulence Assessed through Bayesian Hierarchical Dose-Response Modeling. Journal of Applied Microbiology, 113(2):265-275. [1] 

47. Ryan, M.O., P. J. Duzinsk, P. L. Gurian, C. N. Haas, and J.B. Rose. Accepted. Acceptable Microbial Risk: Benefit-Cost Analysis of a Boil Water Order for Cryptosporidium. (accepted by Journal of American Water Works Association

 

Casman: 

48. Casman, E. A. and B. Fischhoff. (2008). Risk Communication Planning for the Aftermath of a Plague Bioattack. Risk Analysis, 28(5): 1327-1342. [9] 

49. Durham , D.P. and E. A. Casman. (2009). Threshold Conditions for Bubonic Plague Persistence in Urban Rats. Risk Analysis, 29(12):1655-1663.[3] 

 

Project V 

Weber: 

50. Weber, R.O., M. L. Morelli, M. E. Atwood and J. M. Proctor (2006). Designing a Knowledge Management Approach for the CAMRA Community of Science. U. Reimer and D. Karagiannis (Eds.): PAKM 2006, LNAI 4333: 315–325. [7] 

51. Weber, R. O. (2007). Addressing Failure Factors in Knowledge Management. Electronic Journal of Knowledge Management, 5(3): 333-346. [21] 

Gunawardena, S., R. O.Weber and D. E. Agosto. (2010). Finding that Special Someone: Modeling Collaboration in an Academic Context. Journal of Education for Library and Information Science, 51(4): 210-221.[3] 

52. Weber, R.O. and Gunawardena, S. (2012). Representing Scientific Knowledge. Cognition and Exploratory Learning in the Digital Age. CELDA 2012 

53. Gunawardena, S. and R. O. Weber. (2012). Reasoning with Organizational Case Bases in the Absence Negative Exemplars. 2nd Workshop on Process-Oriented Case-Based Reasoning, ICCBR-2012 conference paper 

 

Peer review journal articles under review or in preparations 

1. Lopez, G. U., C. P. Gerba, A. Tamimi, M. Kitajima, S. Maxwell, and J. B. Rose. (2012).Transfer efficiency of bacteria and viruses from porous and nonporous fomites to fingers under different relative humidity. In preparation. 

2. Lopez, G. U., C. P. Gerba, A. Tamimi, M. Kitajima, S. Maxwell, K. A. Reynolds.( 2012). Comparison of two approaches in determining transfer efficiency of Escherichia coli from nonporous fomites to fingers. In preparation 

3. Lopez, G. U. C. P. Gerba, A. Tamimi, M. Kitajima and S. Maxwell. (2012). Survival of bacteria and viruses on surfaces under different humidity. In preparation. 

4. Andrade, A., C.Y. Choi. (2012) Integration of incomplete mixing and axial dispersion into a single water quality modeler of water distribution systems, ASCE Journal of Environmental Engineering, in preparation. 

5. Austin, R, A Andrade, C.Y. Choi. (2012) Experimental verification of combined axial dispersion and incomplete mixing during laminar flow in water distribution systems, ASCE Journal of Environmental Engineering, in preparation. 

6. Hamilton, M., P. L. Gurian, E.A. Casman, and T. Hong. (2012) Responding to Re-aerosolization Risk in the Wake of a Wide-area Anthrax Release. (about to be submitted). 

7. de Bruin, W.B., J. Downs, and E.A. Casman. (2012). Motivations to engage in flu prevention behaviors: The role of perceived effectiveness, difficulty, and concerns about self and others.(about to be submitted) 

8. Tamrakar, S.B. and J.B. Rose. (2012). Dose Response analysis of Pseudomonas aeruginosa: an opportunistic pathogen. (In preparation). 

9. Tamrakar, S.B. and C.N. Haas. (2012) Dose-Response Analysis of Naegleria fowleri. ( under review). 

10. Milbrath , M.O., I.H. Spicknall, J.L. Zelner, C.L. Moe and J.N.Eisenberg Heterogeneity in norovirus shedding duration affects community risk. Epidemiology and Infection.(In Review) 

 

Published articles in the year 2011-2012 

1. Ahamd, F., S. K. Pandey, A. B. Herzog, J. B. Rose, C. P. Gerba and S. A. Hashsham. (2012). Environmental applications and potential health implications of quantum dots. Journal of Nanoparticle Research, 14:1038 

2. Gunawardena, S. and R. O. Weber. (2012). Reasoning with Organizational Case Bases in the Absence Negative Exemplars. 2nd Workshop on Process-Oriented Case-Based Reasoning, ICCBR-2012 

3. Herzog, A.B., A. K. Pandey, D. Reyes-Gasteul, C. P. Gerba, J. B. Rose and S. A Hashsham. (2012). Evaluation of sample recovery for bacteriophage P22 on fomites. Applied Environmental Microbiology,78: 7915-7922 

4. Hong, T. and P.L. Gurian. (2012). Characterizing Bioaerosol Risk from Environmental Sampling. Environmental Science and Technology, 46(12):6714-6722 

5. Hong, T., P.L. Gurian, Y. Huang, and C.N. Haas. (2012). Prioritizing Risks and Uncertainties from Intentional Release of Selected Category A Pathogens. PLoS ONE, 7(3):e32732. 

6. Kitajima, M., Y. Huang, T.Watanabe, H. Katayama and C.N. Haas. (2011). Dose-Response Time Modeling for Highly Pathogenic Avian Influenza A (H5N1) Virus Infection. Letters in Applied Microbiology, 53(4): 438–444. 

7. Mayer, B.T., J. S. Koopman, E. L. Ionides, J. M. Pujol, and J. N. Eisenberg. (2011). A dynamic dose–response model to account for exposure patterns in risk assessment: case study in inhalation anthrax. Journal of Royal Society Interface, 8:57 506-517 [0] 

8. Mayer B.T., J.N. Eisenberg, C.J. Henry, M.G.M. Gomes, E.L. Ionides J.S. Koopman. (2012). Successes and shortcomings of polio eradication: A transmission modeling analysis. American Journal of Epidemiology. (In Press) 

9. Mitchell-Blackwood, J., P. Gurian, R. Lee, and B.Thran. (2012). Variance in Bacillus anthracis Virulence Assessed through Bayesian Hierarchical Dose-Response Modeling. Journal of Applied Microbiology, 113(2):265-275. 

10. Mitchell-Blackwood, J., P. L. Gurian and C. O’Donnell. (2011). Finding Risk-based Switchover Points for Response Decisions for Environmental Exposure to Bacillus anthracisHuman and Ecological Risk Assessment, 17(2): 489-509. 

11. Razzolini, M.T.P. M.H. Weir. M.H. Matte, G.R. Matte, L.N. Fernandes and J.B. Rose. (2011). Risk of Giardia infection for drinking water and bathing in a peri-urban area in St. Paulo, Brazil. International Journal of Environmental Health Research, 21(3), 222-234. 

12. Romero-Gomez, P. and C. Y. Choi. (2011). Axial Dispersion Coefficients in Laminar Flows of Water Distribution Systems. ASCE Journal of Hydraulic Engineering , 137(11):1500-1508. 

13. Romero-Gomez, P., K. E. Lansey, and C. Y. Choi. (2011). Impact of an incomplete solute mixing model on sensor network design, Journal of Hydroinformatics, 13(4):642.651 

14. Ryan, M.O., P. J. Duzinski, P. L. Gurian, C. N. Haas, and J. B. Rose.(2012).Acceptable Microbial Risk: Benefit-Cost Analysis of a Boil Water Order for Cryptosporidium. (accepted by Journal of American Water Works Association

15. Sinclair, R., J. B. Rose, S. A. Hashsham, C. P. Gerba and C. N. Haas. (2012). Selection of microbial surrogates for studying the fate and control of pathogens in the environment. Applied Environmental Microbiology,78:1969-1977 

16. Solon, I., P.L. Gurian, H. Perez. (2012). The Extraction of a Bacillus anthracis Surrogate from Pleated HVAC Filter Samples. Indoor and Built Environment , 21(4):562-567 

17. Tamrakar, S.B. and C.N. Haas. (2011). Dose-Response Model for Rocky Mountain Spotted Fever (RMSF) for Human. Risk Analysis, 31(10): 1610-1621 

18. Tamrakar, S.B., Y. Hunag, and C.N. Haas.(2012). Dose-Response Model for Murine Typhus (Rickettsia typhi): Time Post Inoculation and Host Age Dependency Analysis. BMC Infectious Diseases, 12:77 

19. Teske S.S., Y. Huang, T.A. Bartrand, S.B. Tamrakar, M.H. Weir, and C.N. Haas. (2011). Animal and Human Dose Response Models for Brucella species. Risk Analysis, 31(10):1576-96 

20. Weber, R.O. and Gunawardena, S. (2012). Representing Scientific Knowledge. Cognition and Exploratory Learning in the Digital Age. CELDA 2012 

21. Weir, M.H.; M.T.P. Razzolini, Y. Masago, and J.B. Rose. (2011). Water Reclamation Redesign for Reducing Cryptosporidium Risks at a Recreational Spray Park using Stochastic Models. Water Research, 45(19):6504-6514 

22. Zhao, J., J.N. Eisenberg, I.H. Spicknall, S. Li, and J.S. Koopman . Model Analysis of Fomite Mediated Influenza Transmission. PLoS One (In press )

Technology Transition

New QMRAwiki

CAMRA is developed a central repository in a wiki format, to make quantitative microbial risk assessment information available and accessible to the public. The wiki provides a reliable source of information on microbiological hazards with data, information, and new developments included in the content.  In the future the site will become more interactive and feature a strong tutorial for users to learn QMRA. This site is a valuable source of information for those new to the field of QMRA or for teachers who are looking for new material. The wiki will contribute to building a network of individuals knowledgeable in the field of quantitative microbial risk assessment. The new QMRAwiki is up, and can be accessed here.

Research Partners

Michigan State University
Carnegie Mellon University
Drexel University
Northern Arizona University
University of Arizona
University of California at Berkeley
University of Michigan

Federal Partners

Environmental Protection Agency

CAMRA Resources

Quantitative Microbial Risk Assessment (QMRA) Wiki
The QMRA Wiki is a community portal for current quantitative information and knowledge developed for the Quantitative Microbial Risk Assessment (QMRA) field. It is an evolving knowledge repository intended to be the go to reference source for the microbial risk assessment community.