Computers can predict effects of HIV policies

Policymakers struggling to stop the spread of HIV grapple with "what if" questions on the scale of millions of people and decades of time. They need a way to predict the impact of many potential interventions, alone or in combination. In two papers to be presented at the 2012 International AIDS Society Conference in Washington, D.C., Brandon Marshall, assistant professor of epidemiology at Brown University, will unveil a computer program calibrated to model accurately the spread of HIV in New York City over a decade and to make specific predictions about the future of the epidemic under various intervention scenarios.

"It reflects what's seen in the real world," said Marshall. "What we're trying to do is identify the ideal combination of interventions to reduce HIV most dramatically in injection drug users."

In an analysis that he'll present at 11 a.m. in Session Room 6 on Friday, July 27, Marshall projects 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. Expanding HIV testing would drop the rate only 12 percent to 1.9 per 1,000; increasing drug treatment would reduce the rate 26 percent to 1.6 per 1,000; providing earlier delivery of antiretroviral therapy and better adherence would drop the rate 45 percent to 1.2 per 1,000; and expanding needle exchange programs would reduce the rate 34 percent to 1.4 per 1,000. Most importantly, doing all four of those things would cut the rate by more than 60 percent, to 0.8 per 1,000.

"These results show that a comprehensive set of proven interventions must be scaled up immediately if we are to substantially reduce the spread of HIV among drug users," Marshall said.

Virtual reality, real choices

The model is unique in that it creates a virtual reality of 150,000 "agents," a programming term for simulated individuals, who in the case of the model, engage in drug use and sexual activity like real people.

Like characters in an all-too-serious video game, the agents behave in a world governed by biological rules, such as how often the virus can be transmitted through encounters such as unprotected gay sex or needle sharing.

With each run of the model, agents accumulate a detailed life history. For example, in one run, agent 89,425, who is male and has sex with men, could end up injecting drugs. He participates in needle exchanges, but according to the built-in probabilities, in year three he shares needles multiple times with another injection drug user with whom he is also having unprotected sex. In the last of those encounters, agent 89,425 becomes infected with HIV. In year four he starts participating in drug treatment and in year five he gets tested for HIV, starts antiretroviral treatment, and reduces the frequency with which he has unprotected sex. Because he always takes his HIV medications, he never transmits the virus further.