These materials will be useful in preparation for the NME workshop. We require at the least a working knowledge of the R programming language. If you are inclined, feel free to also browse through the Statnet tutorials and literature listed on this page.


Please read the EpiModel installation instructions below to have the software ready before the workshop. This includes installing R and Rstudio.

R Tutorials

The NME workshop will depend on a good working knowledge of the R statistical software language, which is the basis of the statnet software for the analysis of network data. A good place to start is our R tutorial below.

Statnet Tutorials

We encourage you to explore and practice your R skills with the tutorials for the statnet suite of software for the analysis of networks. You can find these tutorials HERE.


To guide discussion on the use of our network-based models, we recommend that you read the following two papers in Lancet HIV and the Journal of Infectious Diseases before the course. Because many of the methods are not included in the main papers, we suggest that you peruse the Supplementary Technical Appendices of at least one of the papers (you will see there is a good deal of overlap in the appendices between the two papers). In addition, you may be interested in skimming our primary methods paper on EpiModel in the Journal of Statistical Software.

Other Literature

You may be also interested in reading papers on the statistical methods featured in this course and/or applications using these methods.

ERGM Statistical Methods

  1. Hunter DR, Handcock MS, Butts CT, Goodreau SM, Morris M. ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. J Stat Softw. 2008;24(3):nihpa54860. [LINK]

  2. Krivitsky PN, Handcock MS, Morris M. Adjusting for network size and composition effects in exponential-family random graph models. Stat Methodol. 2011;8(4):319–339. [LINK]

  3. Krivitsky PN, Handcock MS. A separable model for dynamic networks. J R Stat Soc Series B Stat Methodol. 2014;76(1):29–46. [LINK]

Applications of ERGMs to Social Science

  1. Morris M, Kurth AE, Hamilton DT, Moody J, Wakefield S. Concurrent Partnerships and HIV Prevalence Disparities by Race: Linking Science and Public Health Practice. Am J Public Health. 2009;99(6):1023–1031. [LINK]

  2. Goodreau SM, Kitts JA, Morris M. Birds of a feather, or friend of a friend? Using exponential random graph models to investigate adolescent social networks. Demography. 2009;46(1):103–126. [LINK]

  3. Carnegie NB, Morris M. Size matters: concurrency and the epidemic potential of HIV in small networks. PloS One. 2012;7(8):e43048. [LINK]

Applied Infectious Disease Models using ERGMs (Same Methods, Pre-EpiModel)

  1. Goodreau SM, Cassels S, Kasprzyk D, Montaño DE, Greek A, Morris M. Concurrent Partnerships, Acute Infection and HIV Epidemic Dynamics Among Young Adults in Zimbabwe. AIDS Behav. 2010;16(2):312–322. [LINK]

  2. Eaton JW, Hallett TB, Garnett GP. Concurrent sexual partnerships and primary HIV infection: a critical interaction. AIDS Behav. 2011;15(4):687–692. [LINK]

  3. Goodreau SM, Carnegie NB, Vittinghoff E, Lama JR, Sanchez J, Grinsztejn B, Koblin BA, Mayer KH, Buchbinder SP. What drives the US and Peruvian HIV epidemics in men who have sex with men (MSM)? PloS One. 2012;7(11):e50522. [LINK]

  4. Goodreau SM, Carnegie NB, Vittinghoff E, Lama JR, Fuchs JD, Sanchez J, Buchbinder SP. Can male circumcision have an impact on the HIV epidemic in men who have sex with men? PLoS One. 2014;9(7):e102960. [LINK]

  5. Carnegie NB, Goodreau SM, Liu A, Vittinghoff E, Sanchez J, Lama JR, Buchbinder S. Carnegie NB1, Goodreau SM, Liu A, Vittinghoff E, Sanchez J, Lama JR, Buchbinder S. J Acquir Immune Defic Syndr. 2015;69(1):119–25. [LINK]

Applied Research using EpiModel

  1. Delaney KP, Rosenberg ES, Kramer MR, Waller LA, Sullivan PS. Optimizing Human Immunodeficiency Virus Testing Interventions for Men Who Have Sex With Men in the United States: A Modeling Study. Open Forum Infect Dis. 2015;2(4): ofv153. [LINK]

  2. Jenness SM, Goodreau SM, Morris M, Cassels S. Effectiveness of Combination Packages for HIV-1 Prevention in Sub-Saharan Africa Depends on Partnership Network Structure. Sexually Transmitted Infections. 2016; 92(8): 619-624. [LINK]

  3. Jenness SM, Goodreau SM, Rosenberg E, Beylerian EN, Hoover KW, Smith DK, Sullivan P. Impact of CDC’s HIV Preexposure Prophylaxis Guidelines among MSM in the United States. Journal of Infectious Diseases. 2016; 214(12): 1800-1807. [LINK]

  4. Jenness SM, Sharma A, Goodreau SM, Rosenberg ES, Weiss KM, Hoover KW, Smith DK, Sullivan P. Individual HIV Risk versus Population Impact of Risk Compensation after HIV Preexposure Prophylaxis Initiation among Men Who Have Sex with Men. PLoS One. 2017; 12(1): e0169484. [LINK]

  5. Goodreau SM, Rosenberg ES, Jenness SM, Luisi N, Stansfield SE, Millett G, Sullivan P. Sources of Racial Disparities in HIV Prevalence among Men Who Have Sex with Men in Atlanta, GA: A Modeling Study. Lancet HIV. 2017; 4(7):e311-e320. [LINK]

  6. Jenness SM, Weiss KM, Goodreau SM, Rosenberg E, Gift T, Chesson H, Hoover KW, Smith DK, Liu AY, Sullivan P. Incidence of Gonorrhea and Chlamydia Following HIV Preexposure Prophylaxis among Men Who Have Sex with Men: A Modeling Study. Clinical Infectious Diseases. 2017; 65(5): 712-718. [LINK]

  7. Vandormael A, Dobra A, Bärnighausen T, de Oliveira T, Tanser F. Incidence rate estimation, periodic testing and the limitations of the mid-point imputation approach. International Journal of Epidemiology. 2018; 47(1): 236-245. [LINK]

  8. Goodreau SM, Hamilton DT, Jenness SM, Sullivan PS, Valencia RK, Wang LY, Dunville RL, Barrios LC, Rosenberg ES. Targeting Human Immunodeficiency Virus Pre-Exposure Prophylaxis to Adolescent Sexual Minority Males in Higher Prevalence Areas of the United States: A Modeling Study. J Adolesc Health. 2018; 62(3): 311-319. [LINK]

  9. Herbeck JT, Peebles K, Edlefsen PT, Rolland M, Murphy JT, Gottlieb GS, Abernethy N, Mullins JI, Mittler JE, Goodreau SM. HIV population-level adaptation can rapidly diminish the impact of a partially effective vaccine. Vaccine. 2018;36(4): 514-520. [LINK]

  10. Luo W, Katz DA, Hamilton DT, McKenney J, Jenness SM, Goodreau SM, Stekler JD, Rosenberg ES, Sullivan P, Cassels S. Development of an Agent-Based Model to Investigate the Impact of HIV Self-Testing Programs for Men Who Have Sex with Men in Atlanta and Seattle. Journal of Medical Internet Research Public Health Surveillance. 2018; 4(2): e58. [LINK]

  11. Ezenwa VO, Archie EA, Craft ME, Hawley DM, Martin LB, Moore J, White L. Host behaviour-parasite feedback: an essential link between animal behaviour and disease ecology. Proc Biol Sci. 2016; 283(1828). [LINK]

  12. Webber QM, Brigham RM, Park AD, Gillam EH, O’Shea TJ, Willis CK. Social network characteristics and predicted pathogen transmission in summer colonies of female big brown bats (Eptesicus fuscus). Behavioral Ecology and Sociobiology. 2016;70(5): 701-12. [LINK].

  13. Goldstein ND, Eppes SC, Mackley A, Tuttle D, Paul DA. A Network Model of Hand Hygiene: How Good Is Good Enough to Stop the Spread of MRSA? Infect Control Hosp Epidemiol. 2017:1-8. [LINK]

  14. White LA, Forester JD, Craft ME. Covariation between the physiological and behavioral components of pathogen transmission: Host heterogeneity determines epidemic outcomes. Oikos. 2018; 127(4): 538-52. [LINK].

  15. Robinson SJ, Barbieri MM, Murphy S, Baker JD, Harting AL, Craft ME, Littnan CL. Model recommendations meet management reality: implementation and evaluation of a network-informed vaccination effort for endangered Hawaiian monk seals. Proceeding of the Royal Society B. 2018; 285(1870): 20171899. [LINK].

  16. Goldstein ND, Jenness SM, Tuttle D, Power M, Paul DA, Eppes SC. Evaluating a neonatal intensive care unit HRSA surveillance programme using agent-based network modeling. Journal of Hospital Infection. 2018. Epub DOI: 10.1016/j.jhin.2018.05.002. [LINK]

  17. Haeussler K, Hout AV, Baio G. A dynamic Bayesian Markov model for health economic evaluations of interventions against infectious diseases. arXiv. arXiv:1512.06881. [LINK].

  18. Amirpour Haredasht S, Tavornpanich S, Jansen MD, Lyngstad TM, Yatabe T, Brun E, Martínez-López B. A stochastic network-based model to simulate the spread of pancreas disease (PD) in the Norwegian salmon industry based on the observed vessel movements and seaway distance between marine farms. Prev Vet Med. 2018. Epub DOI: 10.1016/j.prevetmed.2018.05.019. [LINK]

Last updated: 2018-08-16