Project Title: Progression and Transmission of HIV (PATH) an agent-based simulation of the US population for HIV prevention and intervention analyses

  1. Funding
  2. Student open positions
  3. Journal publications
  4. Policy impact
  5. Model Access
  6. Collaborators 
  7. Description 


NIH R01AI127236, 07/15/2017 – 06/30/2022 LINK
Principal Investigator: Dr. Chaitra Gopalappa
Collaborators: Centers for Disease Control and Prevention

Student open positions:

Graduate student open positions? NO
Undergraduate student open positions? YES
(contact Prof Gopalappa at
Suitable candidates: BS students at UMass- Amherst interested in doing an independent 
study, or Honors thesis in topics related to this project. 
Contact: Send an email to Prof Gopalappa at with your CV 
and one paragraph statement of interest.

Journal publications:

  1.  PATH 4.0 – Agent-based simulation with evolving contact network
    1. (in review) Evolving contact network algorithm: A new network generation algorithm for generating scale-free networks
    2. (Working paper) Agent-based evolving network model: A new simulation technique for simulation of diseases with low prevalence such as HIV
  2. PATH 2.0 – Agent-based simulation with dynamic transmission model
    1. Gopalappa, C., Farnham, P.G., Chen, Y-H., and Sansom, S.L. Combinations of interventions to achieve a national HIV incidence reduction goal: insights from an agent-based model. AIDS, November 28, 2017 – Volume 31 – Issue 18 – p 2533 to 2539
    2. Gopalappa, C., Farnham, P.G., Chen, Y-H., and Sansom, S.L. Progression and Transmission of HIV/AIDS (PATH 2.0): A New Agent-Based Model to Estimate HIV Transmissions in the United States, Med Decis Making 2017 Feb;37(2):224-233. doi: 10.1177/0272989X16668509 LINK
  3. PATH 1.2 – Agent-based simulation without dynamic transmission
    1. Farnham, P., Gopalappa, C., Sansom, S., Costs and effectiveness of early versus late treatment in view of recent guidelines for starting treatment at an earlier HIV stage, Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, JAIDS, 64(2):183-189, 2013.
  4. PATH 1.0- Microsimulation model
    1. Farnham, P.G., Holtgrave, D. R., Gopalappa, C., Angela B. Hutchinson, Stephanie L. Sansom, Lifetime Costs and QALYs Saved from HIV Prevention in the Test and Treat Era, Letter to the Editor, JAIDS, 64(2):e15-e18, 2013
    2. Gopalappa, C., Farnham, P., Hutchinson, A., and Sansom, S., Cost-effectiveness of the National HIV/AIDS Strategy (NHAS) goal of increasing the proportion linking to care at diagnosis, JAIDS, 61 (1):99-105, 2012.

Policy impact:

Results from the PATH model have been used by the CDC in the following reports

  • Dailey AF, Hoots BE, Hall HI, et al. Vital Signs: Human Immunodeficiency Virus Testing and Diagnosis Delays — United States. MMWR Morbidity and Mortality Weekly Report. Dec 2017; 66(47):1300-1306. doi:10.15585/mmwr.mm6647e1
  • Li Z, Purcell DW, Sansom SL, Hayes D, Hall HI. Vital Signs: HIV Transmission Along the Continuum of Care — United States, 2016. MMWR Morb Mortal Wkly Rep 2019; 68:267–272. DOI:
  • McCree, D.H., et. al., ‘HIV acquisition and transmission among men who have sex with men and women: What we know and how to prevent it’, Preventive Medicine, Volume 100, April 2017, Pages 132-134.

Our publications have been cited in the following reports:

Model Access

PATH 4.0 Setup App

PATH 4.0 –  Agent-based evolving network model LINK ( Work in Progress)

PATH 1.0 – Microsimulation model LINK


Centers for Disease Control and Prevention


Approximately 1.2 million people in the United States are living with human immunodeficiency virus (HIV) infection. The Centers for Disease Control and Prevention (CDC) reported that the number of diagnoses in 2014 was 40,493 compared with 43,806 in 2010. Estimated annual HIV incidence in 2013 was 39,000 compared with 43,200 in 2010, based on CD4 test results from people with diagnosed HIV and a CD4 depletion model. The goal of the National HIV/AIDS Strategy (NHAS) is to reduce annual HIV incidence by 25% by 2020.
Work at UMass: We are developing agent-based simulation models to identify optimal combination interventions to achieve the NHAS goal.


agent-based simulation,  network modeling, non-linear neural networks, reinforcement learning, machine learning, probability modeling; Software : Netlogo, MATLAB