Having populations already on hand can help speed up disease-spread simulation and allow modelers and policymakers to keep pace with real outbreaks, including the H1N1 pandemic.

Plus, modelers no longer need to wrangle raw census data for each model and can focus instead on refining their simulations, says Bill Wheaton, a research geographer who oversees the project at RTI.

The synthetic population will also help modelers study the impact of social networks on disease spread. Researchers can track where agents work or go to school, who they live with and who they're likely to meet running errands. Since people get sick when they come into contact with others who've been infected, studying these social patterns in models should be helpful in understanding them in the real world.

Next, the researchers want to create international synthetic populations. They've already finished one for the 110 million people in Mexico, and they're currently working on another one for India.

Modeling many countries is important, says Wheaton, because "infectious disease is not a one-country problem-it spreads around the globe."

Source: NIH/National Institute of General Medical Sciences