Quick Links

Supporting Insitutions

MSU logo
Drexel logo
Temple logo

External Links

History of CAMRA

Microbial risks and the spread of infectious diseases have re-emerged as some of the greatest concerns for our country’s safety and security.  Pathogenic viruses, parasites and bacteria continue to threaten our air, water, soil, food and indoor environments. The ability to measure and identify these microbial hazards as well as understand their ability to survive and spread in the environment are the scientific goals for CAMRA.

CAMRA (the Center for Advancing Microbial Risk Assessment) is an independent research center originally established by the United States Department of Homeland Security and the Environmental Protection Agency, dedicated to advancing laboratory techniques, creating mathematical tools, and developing datasets needed to improve Quantitative Microbial Risk Assessment (QMRA), a science-based framework for addressing disease in our communities from intentional or accidental releases of hazardous biological agents into our indoor and outdoor environments.  Summarizing the threats from multiple microorganisms with diverse biologies in varied scenarios into a comparable estimate – risk – is a valuable product of a QMRA that can help identify the greatest microbial dangers and set priorities for dealing with biological agents of concern (BAC).  QMRA risk estimates are beneficial across many disciplines and used by groups such as public health specialists, emergency managers, first responders, regulators, legislators, and engineers employed to assess and control risk.

QMRA required us to look quantitatively at every step of a microbial exposure pathway, from the first release of a pathogen to the actual human infection.   Additionally, information had to be gathered on how each microorganism moved through the environment, their survival rates on various media, and how people could have been exposed to the microorganism (i.e. skin contact, inhalation, or ingestion).  These measurements gave us a comprehensive view of how environmental contamination (deliberate or accidental) can lead to human infection and reveals key points where interventions can be employed to save lives.  Since QMRA allowed us to incorporate interventions into risk estimates, we can use QMRA as a tool to measure the impact of interventions and identify the best policies and practices to protect public health and safety.

CAMRA’s research was divided into five projects, each specializing in different stages of the QMRA process.

Project I. Exposure: Detection, Fate and Transport of Biological Agents of Concern (BAC)
Project II. Infectious Disease Models for Assessing Microbial Risks for Developing Control Strategies
Project III. Dose-response Modeling and Applications
Project IV. Assessment-Analysis Interface for QMRA
Project V. Knowledge Management, Learning and Discovery for the QMRA Community

For more details, see the CAMRA Annual Reports for:

Year 1, Year 2, Year 3.

Year 6, Year 7

Past Work

Project I – Exposure: Detection, Fate and Transport of Agents

Gerba Lab, University of Arizona
The Gerba Lab coordinated Project I, and measured the survival of microbes on skin, and the transfer of microbes between skin and surfaces.  Both these quantities are vital to modeling disease transmission in the indoor environment.

Choi Lab, University of Arizona
The Choi Lab, located at U of AZ’s Water Village, released microbes into a model water distribution system to study microbial transport.  They compared microbial transport predictions from the innovative computer models they created in Years 1-3 with the results of these release experiments.

Wagner Lab, Northern Arizona University
The Wagner Lab is one of CAMRA’s two BSL3 (Biosafety level 3) laboratories, where BAC can be studied directly.  Here, surrogates (harmless microbes that resemble BAC) are evaluated and their survival rates on surfaces, in soil, and in water are compared to those of the pathogenic microorganism they resemble.  Surrogates that pass these tests can be used to study BAC scenarios without creating a risk to human health.

Hashsham Lab, Michigan State University
The Hashsham Lab specializes in studying the recovery and detection of BAC.  They sequenced DNA taken from frequently and infrequently touched surfaces in a University dormitory to test Project II’s model of influenza transmission via surfaces and air.  They also be tested methods to measure anthrax on surfaces using the surrogate Bacillus thuringiensis.

Nicas Lab, University of California, Berkeley
The Nicas Lab specializes in aerosol studies.  They tested mathematical models of particle movement within a room, using data from an experiment conducted in Year 3.  They also measured the amount of particles from a cough that can reach vulnerable areas of the face (membranes such as the eyes, nasal passages, and mouth).


Project II – Infectious Disease Models for Assessing Microbial Risks and Developing Control Strategies

Eisenberg and Koopman Labs, University of Michigan
The Eisenberg and Koopman Labs incorporated new data from Project I and Project III into their computer models of infectious disease transmission in the indoor environment.  These models combined human movement patterns and environmental contamination levels to see how infections travel in a single venue, multiple venues, and in regional models.  They also collected data on influenza and norovirus outbreaks to test the predictions of their models.


Project III – Dose-Response Modeling and Application

Haas Lab, Drexel University
The Haas Lab is in charge of creating mathematical dose-response relationships for BAC using existing data and data from CAMRA experiments.  They have completed dose-response models for the Category A Biothreat Agents, and they worked on Category B and C Agents, and USDA Select Agents.  They also explored the effect of multiple doses and timing of doses on microbial risks.

Bolin Lab, Michigan State University
CAMRA’s second BSL3 lab, the Bolin Lab performs animal studies to obtain dose-response data for BAC when datasets are not available from the scientific literature.  They conducted experiments to provide multiple dose and time-to-dose response data.


Project IV – The Assessment-Analysis Interface for QMRA

Gurian Lab, Drexel University
The Gurian lab explores innovative ways to apply QMRA concepts to risk management and decision-making.  They used Bayesian Hierarchical Analysis to identify general trends in interspecies survival and dose-response data, developing risk analysis scenarios for Tularemia and pandemic influenza, and factoring risk into cost-benefit analysis for decision models.

Casman Lab, Carnegie Mellon University
The Casman Lab is created a methodology for evaluating the public’s beliefs about microbial threats and identifying potentially dangerous misconceptions.  This methodology was tested with regards to beliefs about influenza by taking concepts from semi-structured interviews and developing a national survey to identify the frequency of these beliefs in the general population.


Project V – Knowledge Management, Transfer, and Learning for the QMRA Community

Weber Lab, Drexel University
The Weber Lab finalized the second version of CAMRA’s knowledge repository, an online data storage and knowledge sharing system that facilitates collaboration between CAMRA researchers and highlights connections between projects.  They also built the core software for the CAMRA Data Warehouse, which contains the assembled QMRA data from five years of CAMRA research and allow future users to add to it, forming a persistent and growing QMRA resource.

Rose Lab, Michigan State University
The Rose lab is in charge of QMRA Education, prepares QMRA workshops for conferences, and hosts the 1-Week QMRA Summer Institute at Michigan State University.  They also use data and tools developed by all CAMRA projects. They completed risk assessments for fomites and key pathogens including the norovirus and  Salmonella.  They focused on beach safety after a sewage spill and waterborne disease after a flood, as well as compiling key QMRA data sets for the CAMRA Data Warehouse.

Projects & Publications


1) Austin, R. G., B. V. Waanders, et al. (2008). "Mixing at cross junctions in water distribution systems. II: Experimental study." Journal of Water Resources Planning and Management-Asce 134(3): 295-302.

2) Bartrand, T. A., M. H. Weir, et al. (2008). "Dose-response models for inhalation of Bacillus anthracis spores: Interspecies comparisons." Risk Analysis 28(4): 1115-1124.

3) Boone, S. A. and C. P. Gerba (2007). "Significance of fomites in the spread of respiratory and enteric viral disease." Appl Environ Microbiol 73(6): 1687-1696.

4) Casman, E. A. and B. Fischhoff (2008). "Risk communication planning for the aftermath of a plague bioattack." Risk Analysis 28(5): 1327-1342.

5) Corella-Barud, V., K. D. Mena, et al. (2009). "Evaluation of neighborhood treatment systems for potable water supply." International Journal of Environmental Health Research 19(1): 49-58.

6) Durham, D. P. and E. A. Casman (2009). "Threshold Conditions for the Persistence of Plague Transmission in Urban Rats." Risk Analysis 29(12): 1655-1663.

7) Greenberg, D. L., J. D. Busch, et al. (2010). "Identifying experimental surrogates for Bacillus anthracis spores: a review." Investig Genet 1(1): 4.

8) Gunawardena, S., R. Weber, et al. (2010). "Finding That Special Someone: Interdisciplinary Collaboration in an Academic Context." Journal of Education for Library and Information Science 51(4): 210-221.

9) Herzog, A. B., S. D. McLennan, et al. (2009). "Implications of Limits of Detection of Various Methods for Bacillus anthracis in Computing Risks to Human Health." Appl Environ Microbiol 75(19): 6331-6339.

10) Hong, T., P. L. Gurian, et al. (2010). "Setting Risk-Informed Environmental Standards for Bacillus Anthracis Spores." Risk Analysis 30(10): 1602-1622.

11) Huang, Y., T. A. Bartrand, et al. (2009). "Incorporating time postinoculation into a dose-response model of Yersinia pestis in mice." Journal of Applied Microbiology 107(3): 727-735.

12) Huang, Y. and C. N. Haas (2009). "Time-Dose-Response Models for Microbial Risk Assessment." Risk Analysis 29(5): 648-661.

13) Huang, Y. and C. N. Haas (2011). "Quantification of the Relationship between Bacterial Kinetics and Host Response for Monkeys Exposed to Aerosolized Francisella tularensis." Appl Environ Microbiol 77(2): 485-490.

14) Huang, Y., T. Hong, et al. (2010). "How Sensitive Is Safe? Risk-Based Targets for Ambient Monitoring of Pathogens." Ieee Sensors Journal 10(3): 668-673.

15) 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(9): 921-938.

16) Jones, R. M., Y. Masago, et al. (2009). "Characterizing the Risk of Infection from Mycobacterium tuberculosis in Commercial Passenger Aircraft Using Quantitative Microbial Risk Assessment." Risk Analysis 29(3): 355-365.

17) Kim, M., C. Y. Choi, et al. (2008). "Source tracking of microbial intrusion in water systems using artificial neural networks." Water Research 42(4-5): 1308-1314.

18) Kitajima, M., Y. Huang, et al. (2011). "Dose-response time modelling for highly pathogenic avian influenza A (H5N1) virus infection." Letters in Applied Microbiology 53(4): 438-444.

19) Li, S., J. N. S. Eisenberg, et al. (2009). "Dynamics and Control of Infections Transmitted From Person to Person Through the Environment." American Journal of Epidemiology 170(2): 257-265.

20) Masago, Y., T. Shibata, et al. (2008). "Bacteriophage P22 and Staphylococcus aureus attenuation on nonporous fomites as determined by plate assay and quantitative PCR." Appl Environ Microbiol 74(18): 5838-5840.

21) Mayer, B. T., J. S. Koopman, et al. (2011). "A dynamic dose-response model to account for exposure patterns in risk assessment: a case study in inhalation anthrax." Journal of the Royal Society Interface 8(57): 506-517.

22) Mitchell-Blackwood, J., P. L. Gurian, et al. (2011). "Finding Risk-Based Switchover Points for Response Decisions for Environmental Exposure to Bacillus anthracis." Human and Ecological Risk Assessment 17(2): 489-509.

23) Pujol, J. M., J. E. Eisenberg, et al. (2009). "The Effect of Ongoing Exposure Dynamics in Dose Response Relationships." Plos Computational Biology 5(6).

24) Razzolini, M. T. P., M. H. Weir, et al. (2011). "Risk of Giardia infection for drinking water and bathing in a peri-urban area in Sao Paulo, Brazil." International Journal of Environmental Health Research 21(3): 222-234.

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

26) Romero-Gomez, P., C. K. Ho, et al. (2008). "Mixing at cross junctions in water distribution systems. I: Numerical study." Journal of Water Resources Planning and Management-Asce 134(3): 285-294.

27) Romero-Gomez, P., K. E. Lansey, et al. (2011). "Impact of an incomplete solute mixing model on sensor network design." Journal of Hydroinformatics 13(4): 642-651.

28) Sinclair, R., S. A. Boone, et al. (2008). "Persistence of category A select agents in the environment." Appl Environ Microbiol 74(3): 555-563.

29) Sinclair, R. G., C. Y. Choi, et al. (2008). "Pathogen Surveillance Through Monitoring of Sewer Systems." Advances in Applied Microbiology, Vol 65 65: 249-269.

30) Sinclair, R. G., P. Romero-Gomez, et al. (2009). "Assessment of MS-2 phage and salt tracers to characterize axial dispersion in water distribution systems." Journal of Environmental Science and Health Part a-Toxic/Hazardous Substances & Environmental Engineering 44(10): 963-971.

31) Solon, I., P. L. Gurian, et al. (2011). "The Extraction of a Bacillus anthracis Surrogate from HVAC Filters." Indoor and Built Environment

32) Song, I., P. Romero-Gomez, et al. (2009). "Experimental Verification of Incomplete Solute Mixing in a Pressurized Pipe Network with Multiple Cross Junctions." Journal of Hydraulic Engineering-Asce 135(11): 1005-1011.

33) Spicknall, I. H., J. S. Koopman, et al. (2010). "Informing Optimal Environmental Influenza Interventions: How the Host, Agent, and Environment Alter Dominant Routes of Transmission." Plos Computational Biology 6(10).

34) Tamrakar, S. B. and C. N. Haas (2008). "Dose-response model for Burkholderia pseudomallei (melioidosis)." Journal of Applied Microbiology 105(5): 1361-1371.

35) Tamrakar, S. B. and C. N. Haas (2008). "Dose-response model for Lassa virus." Human and Ecological Risk Assessment 14(4): 742-752.

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

37) Tamrakar, S. B., A. Haluska, et al. (2011). "Dose-Response Model of Coxiella burnetii (Q Fever)." Risk Analysis 31(1): 120-128.

38) Teske, S. S., Y. Huang, et al. (2011). "Animal and Human Dose-Response Models for Brucella Species." Risk Analysis 31(10): 1576-1596.

39) Weber, R. O. (2007). "Addressing Failure Factors in Knowledge Management." Electronic Journal of Knowledge Management 5(3): 333-346.

40) Weber, R. O., M. L. Morelli, et al. (2006). "Designing a Knowledge Management Approach for the CAMRA Community of Science." Practical Aspects of Knowledge Management. Lecture Notes in Computer Science 4333: 315-325.

41) Weir, M. H. and C. N. Haas (2009). "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.

42) Weir, M. H., M. T. P. Razzolini, et al. (2011). "Water reclamation redesign for reducing Cryptosporidium risks at a recreational spray park using stochastic models." Water Research 45(19): 6505-6514.

43) Yoon, J. Y., J. H. Han, et al. (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.

44) Zelner, J. L., A. A. King, et al. (2010). "How Infections Propagate After Point-Source Outbreaks An Analysis of Secondary Norovirus Transmission." Epidemiology 21(5): 711-718.



  1. Project I -- Exposure: Detection, Fate and Transport of Agents
    Charles P. Gerba, University of Arizona

    View project details

  2. Project II -- Infectious Disease Models for Assessing Microbial Risks and Developing Control Strategies
    Joseph N.S. Eisenberg, University of Michigan-Ann Arbor

    View project details

  3. Project III -- Dose Response Assessment
    Charles N. Haas, Drexel

    View project details

  4. Project IV -- The Assessment-analysis Interface
    Patrick Gurian, Drexel

    View project details

  5. Project V -- Knowledge Management, Transfer, and Learning
    Rosina Weber, Drexel

    View project details


Theses / Dissertations

  • Pedro Romero-Gomez, University of Arizona, 2010. Doctoral Dissertation. Transport phenomena in drinking water systems. Supervised by Professor Christopher Choi.
  • Jade Mitchell-Blackwood, Drexel University, 2010. Using Analytic Models to Address Uncertainty and Establish Thresholds for Risk-Based Responses to Pathogenic Agents in the Environment. Doctoral Dissertation. Supervised by Professor Patrick Gurian.
  • Yin Huang, Drexel University, 2010. Doctoral Dissertation. Incorporating Time to Response into Dose-Response Models Used in Quantitative Microbial Risk Assessment. Supervised by Professor Charles Haas.
  • Ian Solon, Drexel University, 2010. Master Thesis. The extraction of a B. anthracis Surrogate from Pleated HVAC Filters. Supervised by Professor Patrick Gurian.
  • Ms. Surakshya Dhakal, University of California Berkeley, 2009 Masters Thesis. Exploring the Relationships between Time-to-Mixing, Turbulence Intensity, and the Turbulent Diffusion Coefficient. Supervised by Professor Mark Nicas.
  • Tao Hong, Drexel University, 2009 Masters Thesis. Estimating risk of exposure to Bacillus anthracis based on environmental concentrations. Supervised by Patrick Gurian
  • Mark H. Weir, Drexel University, 2009. Doctoral Dissertation. Development of a Physiologically Based Pathogen Transport and Kinetics Model for Inhalation of Bacillus anthracis spores. Supervised by Charles N. Haas
  • Henley, J. B. 2008. Thesis for Master of Science. Determining the Inactivation Rates of Viruses on Indoor Surfaces. College of Agriculture and Life Sciences, Department of Soil, Water, and Environmental Science, University of Arizona.
  • Herzog, A. 2008. Thesis for Master of Engineering. Factors to Consider in the Evaluation of Risk at the Environmental Detection Limit. Center for Microbial Ecology, Department of Civil and Environmental Engineering, Michigan State University
  • Jones, R. M. 2008. Dissertation for Doctor of Philosophy. Experimental Evaluation of a Markov Model of Contamination Transport in Indoor Experiments with Application to Tuberculosis Transmission in Commercial Passenger Aircraft.  School of Public Health, Division of Environmental Health Sciences, University of California, Berkeley.

Book Chapters

  • Pepper, I. L., C. Y. Choi, and C. P. Gerba. Microorganisms and bioterrorism. In: Environmental Microbiology. R. M. Maier, I. L. Pepper and C. P. Gerba, eds. pp. 565-574. Academic Press, San Diego.

Un-refereed documents

  • White Paper Submitted in Support of the Physiologic Assessment of Microbial Effects (PhAME) Project.
  • White-Paper was submitted to USACCHPM and is currently pending security clearance for public release of the information. Jade Mitchell-Blackwood and Patrick L. Gurian, Development of Dose-Response Curves for Bacillus anthracis (Inhalation Anthrax) Using a Bayesian Approach on Historic Data.
  • White Paper Submitted in Support of the Physiologic Assessment of Microbial Effects (PhAME) Project, Limited Distribution through the U.S. Army Center for Health Promotion and Preventive Medicine, Environmental Health Risk Assessment Program (MCHB-TS-REH), Aberdeen Proving Ground, Maryland (2008).
  • Masago, Y, Jones, R, Bartrand, TA, Haas, CN, Rose, JB.  2008.  Estimates of risk associated with a tuberculosis patient and air travel. CAMRA TB Alert Report.
  • Rose, J. B., C. N. Haas, P. L. Gurian, and J. S. Koopman. 2008. Instruction manual for quantitative microbial risk assessment (QMRA). CAMRA 3rd QMRA Summer Institute, Michigan State University, East Lansing, MI.
  • Herzog, A., C. Rodriguez, K. Enger, M. Milbrath, O. Bucher, P. Okelo, S. Luster-Teasley, and Y. Huang. 2008. Risk Assessment on Anthrax release in IRS Building. Group A Case Study: CAMRA 3rd QMRA Summer Institute, Michigan State University, East Lansing, MI.
  • Pandey, A., W. Betacourt, A. Baynton, A. Wright, K. Bush, S. Khan, K. Shaw, A. Coulliette. 2008. Adventure Vacations: Leaving Norovirus at the Dock: A QMRA Assessment of Norovirus Outbreaks on Houseboats. Group B Case Study: CAMRA 3rd QMRA Summer Institute, Michigan State University, East Lansing, MI.
  • Mendoza-Sanchez, I., F. Simmons, C. O’Donnell, M. Sato, A. Rajić, B. Mayer, A. Jenkins, J. B. Rose, and G. Medema. Tularemia in Water: A Quantitative Microbial Risk Assessment. Group C Case Study: CAMRA 3rd QMRA Summer Institute, Michigan State University, East Lansing, MI.
  • Gurian, P., I. Young, M. T. Pepe Razzolini, R. Johnson, B. Schmidt, C. Flemming, T. Hong, and A. Smith. Risk Assessment for H5N1 Influenza A: Border Inspection of Smuggled Goods Exposure. Group D Case Study: CAMRA 3rd QMRA Summer Institute, Michigan State University, East Lansing, MI.
  • Shibata, T., Gurian, P, Rose JB., Haas, CN., Choi, C., Nicas, M., and Koopman, J., 2007. Instruction manual for quantitative microbial risk assessment (QMRA). CAMRA 2nd QMRA Summer Institute, Michigan State University, East Lansing, MI.