The spread of infectious diseases can be mitigated by properly targeted public policies that actually change people’s behaviors. HIV infections and their distribution are particularly susceptible to how they are addressed by public health officials, but it usually takes years to measure the actual results of the policies taken.
In order to help predict which set of policies is most effective, Brandon Marshall, assistant professor of epidemiology at Brown University, developed a computer simulation that involves thousands of virtual humans with unique behavioral patterns. At last week’s International AIDS Society Conference in Washington, D.C., Marshall presented findings from thousands of completed simulations of how New York City’s HIV/AIDS population becomes infected. By using historical data gathered in years past, Marshall was able to tune and calibrate the algorithms to correctly predict the past, and so hopefully the future.
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.
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.
To calibrate the model, Marshall and his colleagues found the best New York City data they could about how many people use drugs, what percentage of people were gay or lesbian, the probabilities of engaging in unprotected sex and needle sharing, viral transmission, access to treatment, treatment effectiveness, participation in drug treatment, progression from HIV infection to AIDS, and many more behavioral, social and medical factors. They also continuously calibrated it until the model could faithfully reproduce the infection rates among injection drug users that were known to occur in New York between 1992 and 2002.
Press release: Computers can predict effects of HIV policies…