BIOGRAPHICAL SKETCH James S. Koopman

EDUCATION/TRAINING INSTITUTION AND LOCATION

University of Michigan, Ann Arbor, MI University of Michigan, Ann Arbor, MI University of California, Los Angeles, CA University of Washington, Seattle, WA

DEGREE (if applicable)

YEAR(s)

FIELD OF STUDY

B.S. M.D. Diploma M.P.H

1969 1969 1972 1976

Biology Medicine Pediatrics Epidemiology

A. Personal Statement I have a combination of field experiences and theoretical analysis capacities that prepare me for a role on the WHO HIV vaccine committee. From 1969 through 1986 I dedicated myself to advancing practical field epidemiology in the United States and in developing countries. During that time I had PAHO consulting positions in Chile, Bolivia, Uruguay, and Colombia. I served as a WHO consultant in smallpox eradication in India directing eradication efforts in Azamgarh district U.P. I spent 4 years developing innovative surveillance programs in Cali, Colombia within the local health department. And under US CDC and Mexican Secretariate of Health auspices, I developed innovative surveillance programs and trained a group of epidemiologists who went on to make those programs the cornerstone of highly successful disease control efforts in Mexico. In the US during that time I served as EIS officer and acting state epidemiologist in the state of Washington and I worked to supplement State of Michigan Surveillance programs with University of Michigan research programs and personnel. Upon returning from Mexico in 1986 I dedicated myself to “developing theory that serves public health”. I made the decision to do that because in all of my work in Latin America, Asia, and the US I realized that the theoretical foundations for public health actions needed a firmer footing in infection transmission system theory, models, and data. Infection control actions were too often being undertaken with only a vague understanding of system effects on transmission dynamics. My collaborations with Ira Longini, who I supported both in Cali and at Michigan after he got his PhD, helped me develop a vision as to where infection transmission system analyses needed to go. I initially decided in 1986 to focus on HIV for building the needed theoretical foundation since I thought that having a definable contact was important. Although I continued to work on enteric and respiratory infection transmission systems and how vaccines worked with such systems, back in the late 80’s contact was defined in too abstract a manner for enteric and respiratory infection transmission systems to serve as model systems that could be related well to data. After a few years most of my research was directed to infections other than HIV. But now I am wholly rededicated to this infection. Our initial work with HIV pointed out that acute infection played a crucial role in the transmission system. Nearly twenty years after that work, however, we are still ignorant about which cases are generating HIV transmissions. Cohen’s NEJM review this year graphed four papers that were relevant to how much transmission was coming from MSM during acute infection. Two of those were my work. But those two were only expressions of compatible ranges. Our great ignorance about who is doing the infecting is easy to understand. We diagnose infection long after it occurs and we don’t identify who the source case is. But I am happy to say that new stronger theory, new analytic methods, and above all, new data from deep sequencing viruses are giving us greatly increased power to determine the characteristics of the source cases of HIV infection. It is not our work with the Montreal data that is opening up this new knowledge. It is data from Michigan that is making the difference. The new methods will appear in Jan 2012 in Genetics by Erik Volz. These allow us to infer how many transmissions are coming from people who are diagnosed or undiagnosed, in different stages of infection, or are between different age, race, and geographic groups. Those new methods can calculate likelihoods for any coalescent given any transmission system model. This is highly relevant to evaluation of HIV vaccines and decisions as to how to use HIV vaccines with different characteristics. The HIV transmission system has many complexities that markedly affect the assessment of vaccine effects. These complexities include patterns of partnership duration and concurrency, patterns of insertive and receptive behaviors, group specific contact patterns, and patterns of sex and partnership

formation by individuals over time. If vaccine effects are evaluated from trial populations experiencing these effects but where they are not integrated into the analysis, important vaccine effects could be missed. If the analytic models wrongly specify these effects, distortion of estimated effects will occur. The key to making good inferences in such a situation is to correctly assess the robustness of the inferences made to model misspecification and to formulate estimators that are robust. In recent conference presentations we have demonstrated these effects. Two of those are now almost in press. Another from the recent Epidemics conference in Boston is in a state of revision and not yet submitted. What we have shown is that most MSM exhibit volatile or episodic risk behaviors. This volatility or episodic risk has strong effects on population risks, fraction of transmissions from acute infection, and the potential for vaccines with different effects on natural history of infection or transmissibility of any resulting infections to alter population transmission patterns. Most importantly, it makes the pattern of genetic relatedness in HIV strains provide a strong signal as to how vaccination has altered population transmission patterns. The signal should be far clearer that what would be perceived with current vaccine trial designs that are far more expensive. While I do hope to play a significant role with the young people who are carrying on this work, I think my role as a senior investigator is more important at higher levels, such as on this WHO Vaccine committee. I think it could be important for this committee to be served by someone with both the long term understanding of HIV transmission system theories that I have as well as the understanding of the transmission system estimation of effects that I have. My service on QUIVER was a learning experience that has better prepared me to make positive contributions at this level. B. Positions and Honors Positions and Employment 1970-1972 Pediatrics residence at Harbor General Hospital, Los Angeles, CA. 1972-1974 EIS Officer in the State of Washington. Acting state epidemiologist toward end of period. 1974 WHO consultant with the smallpox eradication program in India. 1975-1978 Diarrheal disease and nutrition investigations in Cali, Columbia with a position in the local health department and as a visiting scientist with CIDEIM. 1978-1983 Assistant Professor of Epidemiology, University of Michigan. 1983-1991 Associate Professor at the University of Michigan. 1984-1986 CDC fellow establishing a national field epidemiology and investigation service in Mexico 1991- Present Full Professor, Department of Epidemiology, University of Michigan. 1995- Present Founding member U of M Center for the Study of Complex Systems Honors

1994 2005

Howard Temin prize for best paper in Journal of AIDS Ken Rothman prize for best paper in Epidemiology

C. Selected relevant peer-reviewed publications (in chronological order out of about 150) 1. Kim JH, Koopman JS. HIV Transmissions by Stage in Dynamic Sexual Partnerships. Journal of Theoretical Biology 2012. NIHMS349965 2. Zhang X, Romero-Severson EO, Henry C, Zhong L, Alam SJ, Volz EM, Koopman JS. Episodic HIV risk behavior can greatly amplify HIV prevalence and the fraction of transmissions from acute HIV infection. Statistical Communications in Infectious Diseases 2012. 3. Brenner BG, Roger M, Stephens D, Moisi D, Hardy I, Weinberg J, Turgel R, Charest H, Koopman J, Wainberg MA; Montreal PHI Cohort Study Group. Transmission clustering drives the onward spread of the HIV epidemic among men who have sex with men in Quebec. J Infect Dis. 2011 Oct 1;204(7):11159. PMC3164430 4. Mayer BT, Koopman JS, Ionides EL, Pujol JM, Eisenberg JN. A dynamic dose-response model to account for exposure patterns in risk assessment: a case study in inhalation anthrax. J R Soc Interface. 2011 Apr 6;8(57):506-17. PMC3061128

5. Kretzschmar M, Gomes MG, Coutinho RA, Koopman JS. Unlocking pathogen genotyping information for public health by mathematical modeling. Trends Microbiol. 2010 Sep;18(9):406-12. 6. Spicknall IH, Koopman JS, Nicas M, Pujol JM, Li S, Eisenberg JN. Informing optimal environmental influenza interventions: how the host, agent, and environment alter dominant routes of transmission. PLoS Comput Biol. 2010 Oct 28;6(10):e1000969. PMC2965740 7. Pujol JM, Eisenberg JE, Haas CN, Koopman JS. The effect of ongoing exposure dynamics in dose response relationships. PLoS Comput Biol. 2009 Jun;5(6):e1000399. PMC2685010 8. Li S, Eisenberg JN, Spicknall IH, Koopman JS. Dynamics and control of infections transmitted from person to person through the environment. Am J Epidemiol. 2009 Jul 15;170(2):257-65. 9. Koopman JS. Infection Transmission through Networks (Chapter 13) in Biological Networks Ed. F. Kepes. World Scientific, Singapore. 2007:1-58. [ http://www.worldscibooks.com/lifesci/6459.html ] 10. Koopman JS, Simon CP, Riolo CP. When to control endemic infections by focusing on high-risk groups. Epidemiology. 2005 Sep;16(5):621-7. 11. Koopman JS. Mass Action and System Analysis of Infection Transmission. In Ecological Pardigms Lost: Routes to Theory Changes. Ed. K. Cuddington and B.E. Beisner in the Theoretical Ecology Series (Series Ed. A. Hastings). Academic Press 2005. 12. Koopman JS. Infection transmission science and models. Jpn J Infect Dis. 2005 Dec;58(6):S3-8. 13. Riggs T, Koopman JS. Maximizing statistical power in group-randomized vaccine trials. Epidemiol Infect. 2005 Dec;133(6):993-1008. PMC2870333 14. Simon CP, Koopman JS. A Complex Systems Approach to Understanding the HIV/AIDS Epidemic. Mathematics for Industry: Challenges and Frontiers, A Process View: Practice and Theory. Ed. David R. Ferguson and Thomas J. Peters. Society for Industrial and Applied Mathematics. 2005;199-221. 15. Koopman JS, Lin X, Chick SE, Gilsdorf J. Transmission Model Analysis of Nontypeable Haemophilus influenzae Immunity Effects on Transmission and Pathogenicity. In Handbook of Operations Research / Management Science Applications in Health Care. Ed. F Sainfort, M Brandeau, W Pierskalla. Kluwer. March 2004. 16. Koopman J. Modeling infection transmission. Annu Rev Public Health. 2004;25:303-26. Review. 17. Riggs TW, Koopman JS. A stochastic model of vaccine trials for endemic infections using group randomization. Epidemiol Infect. 2004 Oct;132(5):927-38. PMC2870181. 18. Chick SE, Soorapanth S, Koopman JS. Microbial Risk Assessment for Drinking Water. In Handbook of Operations Research / Management Science Applications in Health Care. Ed. F Sainfort, M Brandeau, W Pierskalla. Kluwer. July 2003. 19. Koopman JS, Lin X, Chick SE, Gilsdorf J. Transmission Model Analysis of Nontypeable Haemophilus influenzae Immunity Effects on Transmission and Pathogenicity. In Handbook of Operations Research / Management Science Applications in Health Care. Ed. F Sainfort, M Brandeau, W Pierskalla. Kluwer. June 2003. 20. Koopman JS, Chick SE, Simon CP, Riolo CS, Jacquez G. Stochastic effects on endemic infection levels of disseminating versus local contacts. Math Biosci. 2002 Nov-Dec;180:49-71. 21. Koopman J. Epidemiology. Controlling smallpox. Science. 2002 Nov 15;298(5597):1342-4. 22. Koopman JS. Modeling infection transmission- the pursuit of complexities that matter. Epidemiology. 2002 Nov;13(6):622-4. 23. Sander LM, Warren CP, Sokolov IM, Simon C, Koopman J. Percolation on heterogeneous networks as a model for epidemics. Math Biosci. 2002 Nov-Dec;180:293-305. 24. Simon CP, Koopman JS. Infection Transmission Dynamics and Vaccination Program Effectiveness As A Function of Vaccine Effects In Individuals. IMA volume 126; Mathematical Approaches for Emerging and Reemerging Infectious Diseases: Models, Methods and Theory. Ed. S. Blower, C. Castillo-Chavez, P. van den Driessche and A.A. Yakubu. 2002 Springer, New York,143-157. 25. Koopman JS, Jacquez G, Chick SE. New data and tools for integrating discrete and continuous population modeling strategies. Ann N Y Acad Sci. 2001 Dec;954:268-94. Review. 26. Riolo CS, Koopman JS, Chick SE. Methods and measures for the description of epidemiologic contact networks. J Urban Health. 2001 Sep;78(3):446-57. Review. 27. Chick SE, Adams AL, Koopman JS. Analysis and simulation of a stochastic, discrete-individual model of STD transmission with partnership concurrency. Math Biosci. 2000 Jul;166(1):45-68.

28. Koopman JS, Chick SE, Riolo CS, Adams AL, Wilson ML, Becker MP. Modeling contact networks and infection transmission in geographic and social space using GERMS. Sex Transm Dis. 2000 Nov;27(10):617-26. 29. Koopman JS, Lynch JW. Individual causal models and population system models in epidemiology. Am J Public Health. 1999 Aug;89(8):1170-4. PMC1508689 30. Adams A, Barth-Jones DC, Chick SE, Koopman JS. Simulations to Evaluate HIV Vaccine Trial Methods. Simulation. 1998;71(4):228-241. 31. Welch G, Chick SE, Koopman J. Effect of Concurrent Partnerships and Sex-Act Rate on Gonorrhea Prevalence. Simulation 1998; 71(4):242-249. 32. Koopman JS, Jacquez JA, Simon CP, Foxman B, Pollock S, Barth-Jones D, Adams A, Welch G, Lange K. The Role of Primary HIV Infection in the Spread of HIV Through Populations. JAIDS and HR 1997;14:249-258. 33. Jacquez JA, Simon CP, and Koopman JS. Core groups and the Ro's for subgroups in heterogeneous SIS and SI models. In Epidemic Models: Their Structure and Relationship to Data. Mollison D. (ed.) Cambridge University Press, 1995; p. 279-301. 34. Koopman JS, Little RJ. Assessing HIV vaccine effects. Am J Epidemiol. 1995 Nov 15;142(10):1113-20. 35. Jacquez JA, Koopman JS, Simon CP, Longini IM Jr. Role of the primary infection in epidemics of HIV infection in gay cohorts. J Acquir Immune Defic Syndr. 1994 Nov;7(11):1169-84. Review. 36. Koopman JS, Longini IM Jr. The ecological effects of individual exposures and nonlinear disease dynamics in populations. Am J Public Health. 1994 May;84(5):836-42. PMC1615035 37. Koopman JS, Simon CP, Jacquez JA. Assessing Effects of Vaccines on Contagiousness and Risk Factors for Transmission. In Modeling the AIDS Epidemic: Planning, Policy, and Prediction. Kaplan EH and Brandeau ML (Eds.) Raven Press, New York, 1994. 38. Lindblade K, Foxman B, and Koopman JS. Heterosexual Partnership Characteristics of Sorority Women. Internation Journal of STD and AIDS. Internation J STD & AIDS. 1994;5:37-40. 39. Jacquez JA, Simon CP, and Koopman JS. Observations on CD4 Count Progressions in Different Stages of HIV Infection. In Epidemic Models: Their Structure and Relationship to Data. Mollison D. (ed.) Cambridge University Press, 1994. Presented at the NATO Workshop on Human Infections and Transmission Models. Cambridge, England, March 31, 1993. 40. Koopman JS, Haber MJ, Longini IM, Simon CP, and Jacquez JA. Using Transmission Models to Assess Risk Factors for Transmission. In Epidemic Models: Their Structure and Relationship to Data. Mollison D. (ed.) Cambridge University Press, 1994. Presented at the NATO Workshop on Human Infections and Transmission Models. Cambridge, England, March 30, 1993. 41. Simon CP, Jacquez JA, and Koopman JS. The Liapunov Function Approach to Computing Ro. In Epidemic Models: Their Structure and Relationship to Data. Mollison D. (ed) Cambridge University Press, 1994. Presented at the NATO Workshop on Human Infections and Transmission Models. Cambridge, England, April 1, 1993. 42. Pope SK, Koopman JS, Ostrow D, Joseph J, Fletcher D, Prokopowicz G, Natale J. The link between sexually transmitted disease clinics and HIV counseling and testing centers: who is not getting referred? AIDS Educ Prev. 1992 Fall;4(3):219-26. 43. Jacquez JA, Simon CB, and Koopman JS. The Reproduction Number in Deterministic Models of Contagious Diseases. Comments on Theoretical Biology 1991;2(3):159-209. 44. Koopman JS, Longini IM Jr, Jacquez JA, Simon CP, Ostrow DG, Martin WR, Woodcock DM. Assessing risk factors for transmission of infection. Am J Epidemiol. 1991 Jun 15;133(12):1199-209. 45. Simon MS, Weyant RJ, Asabigi KN, Zucker L, and Koopman JS. Medical Student Attitudes Towards the Treatment of HIV Infected Patients. AIDS Education and Prevention 1991;3:124-132. 46. Joseph JG, Adib SM, Koopman JS, Ostrow DG. Behavioral change in longitudinal studies: adoption of condom use by homosexual/bisexual men. Am J Public Health. 1990 Dec;80(12):1513-4. PMC1405098 47. Koopman JS. Comment on Kaplan's Critique of Masters, Johnson, and Kolodny. J Sex Res. 1990; 27(4). 48. Sattenspiel L, Koopman J, Simon C, Jacquez JA. The effects of population structure on the spread of the HIV infection. Am J Phys Anthropol. 1990 Aug;82(4):421-9.

49. Jacquez JA, Simon CP, and Koopman JS. Structured Mixing: Heterogeneous Mixing by the Definition of Activity Group. In Mathematical and Statistical Approaches to AIDS Epidemiology. Castillo-Chavez C, ed. Springer-Verlag Lecture Notes in Biomathematics. 1989; 83:301-315. 50. Koopman JS, Simon CP, Jacquez JA, and Park TS. Selective Contact Within Structured Mixing Application to HIV. In Mathematical and Statistical Approaches to AIDS Epidemiology. CastilloChavez C, ed. Springer-Verlag Lecture Notes in Biomathematics. 1989; 83:316-349. 51. Jacquez JA, Koopman JS, Simon C, Sattenspiel L, Perry T. Modeling and the Analysis of HIV Transmission: The Effect of Contact Patterns. Math Biosci. 1988;92:119-199. 52. Koopman J, Simon C, Jacquez J, Joseph J, Sattenspiel L, Park T. Sexual partner selectiveness effects on homosexual HIV transmission dynamics. J Acquir Immune Defic Syndr. 1988;1(5):486-504. 53. Koopman JS. Analyzing the joint effects of two antibodies and the design of molecularly engineered vaccines. J Theor Biol. 1985 Oct 21;116(4):569-85.

D. Research Support Ongoing Research Support NIH 04/01/08-06/30/13 “HIV Risk Dynamics, Genetics Patterns and Control” The major goal of this project is to develop new methods to use HIV sequences from a population to help infer key transmission system parameters such as transmission rates by time since infection as well as the expected outcomes given different policies for infection control. Sequences gathered under the Quebec genotyping program are to be analyzed using these methods in order to inform particular policy decisions such as switching to early treatment. Role: Principal Investigator NIH 07/01/11-06/30/13 Supplement to study deep sequences as a basis for HIV surveillance and for use in the methods developed by Volz to estimate fractions of transmissions from different classes of individuals by fitting coalescent models