Modeling Flu and Other Infectious Diseases Gets More Real with Virtual Populations

To help computational modelers who study the spread of infectious diseases, including flu, researchers supported by the National Institutes of Health at RTI International in North Carolina created a synthetic population mirroring U.S. demographics. Now theyve added another layer of realism: where the virtual citizens live.

While it may sound more like a tool for virtual gaming, synthetic populations are a very useful tool for disease modeling. By incorporating agents who represent U.S. citizens, modelers can better simulate the spread of an infectious outbreak through a community and identify the best ways to intervene. They also can use synthetic populations to study how certain behaviors may speed up or slow down the spread of an outbreak.

RTIs synthetic population was developed as part of NIHs Models of Infectious Disease Agent Study (MIDAS) and has been used by MIDAS researchers to model the spread of seasonal and pandemic flu and methicillin-resistant Staphylococcus aureus (MRSA).

Until now, the RTI synthetic population was based primarily on 2000 census data, such as household sizes, family incomes and residents ages. Houses werent placed in the middle of lakes or airports, but they were randomly distributed across census blocks.

With the availability of geospatial data from satellite imaging, remote sensing and other technologies, the researchers now have more realistically plotted where the virtual residents likely reside. They used LandScan USA, a collection of data about road locations, land cover and slope and nighttime lights that also approximates where people do and do not live. Houses, for instance, are typically built near roads and not on mountains.

"If you know more specifically where houses are located," said John Boos, a geospatial research analyst at RTI who incorporated the LandScan USA data, "you can much more accurately bring spatial processes into modeling activities."

For disease modelers, this means more realistically simulating the spread of infectious agents, such as pathogens and disease-carrying insects; studying differences between rural and urban transmission patterns; and estimating access to healthcare facilities or other community resources that may factor into different intervention strategies.