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.
That level of individual detail allows for a detailed examination of transmission networks and how interventions affect them.
"With this model you can really look at the microconnections between people," said Marshall, who began working on the model as a postdoctoral fellow at Columbia University and has continued to develop it since coming to Brown in January. "That's something that we're really excited about."
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 r
|Contact: David Orenstein|