Computer Simulation Models Effects of HIV Policies

Brandon Marshall (Brown University)

A Brown University epidemiologist developed a computer simulation that can model the spread of HIV in New York City, under various scenarios of interventions. Brandon Marshall (pictured left) discusses his work in two sessions at this week’s International AIDS Society Conference in Washington, D.C.

The model creates a community of individual actors, who engage in activities that affect the transmission of HIV, such as sexual encounters and intravenous drug use. These 150,000 actors, or agents as they are called in simulations, behave in a world governed by quantifiable biological rules for risky behaviors, such as unprotected sex and sharing of drug needles.

The model also quantifies behaviors that lower the risk of HIV infection, such as participation in drug rehabilitation and needle exchanges, as well as antiretroviral treatments. Each run of the model adds more detail to each individual’s life story. That level of individual detail enables the model to provide a granular examination of transmission networks and the impact of various interventions on those networks.

To populate the model, Marshall and colleagues secured data from New York City on conditions and activities related to HIV infection, such as numbers of drug users, percentage of gay or lesbian people, probabilities of engaging in unprotected sex and needle sharing, viral transmission, access to treatment, treatment effectiveness, participation in drug treatment, and progression from HIV infection to AIDS. The data are stored on a supercomputer at Brown, which the researchers — who include Magdalena Paczkowski, Lars Seemann, Barbara Tempalski, Enrique Pouget, Sandro Galea, and Samuel Friedman — have run thousands of times, and have calibrated to reproduce the infection rates among injection drug users known to occur in New York between 1992 and 2002.

In one of the conference presentations, Marshall estimates that with no change in New York City’s current programs, the infection rate among injection drug users will be 2.1 per 1,000 in 2040. The projections also show individual interventions such as expanded HIV testing, more narcotics treatments, earlier delivery of antiretroviral therapy and better drug adherence, and expanded needle exchange programs would reduce infection rates from 12 to 45 percent. Doing all four interventions together would cut the infection rate by more than 60 percent, to 0.8 per 1,000.

Commenting on the results showing that even all four actions would still leave the infection rate at nearly 40 percent of where they started, Marshall says, “That speaks to how hard we have to work to make sure that drug users can access and benefit from proven interventions to reduce the spread of HIV.”

The Brown team continues to refine the model, adding other aspects such as an analysis of the cost effectiveness of each intervention and their combinations.

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