Researchers at Virginia Tech are building a “Virtual America," mirroring the country’s population with complex algorithms intended to replicate existing social networks in order to predict contagion outbreaks and other large-scale human patterns. “EpiSmidemics is a highly scalable, parallel algorithm to simulate the spread of contagion in large, realistic social contact networks using individual-based models. EpiSimdemics is specifically designed to scale to social networks with 100-300 million individuals,” says a research abstract. Using demographic data and Census information, the researchers have constructed a virtual population of 100 million whose attributes mirror the real one. Their goal is to essentially simulate America in order to better understand the large-scale patterns humans create and are affected by.
“The model’s makers hope the simulation will shed light on the effects of human comings and goings, such as how a contagion spreads, a fad grows, or traffic flows,” writes IEEE Spectrum’s Sandra Upson.
Within the next six months, the program will be simming all of America’s 300 million inhabitants.
The simulated people are assigned education levels, jobs, and income based on real-world stats. Each individual is a series of algorithms based on real world demographics, like what store they’ll do their shopping at, what kind of commute they have, or whether they live with others or alone. As many as 163 variables can come into play for any individual, which the researchers claim effectively mirror populations greater than 1500.
The difficulty is simulating the unpredictability of human relations. “The lack of symmetry and regularity makes these types of problems very different from traditional physics problems that require large computing power,” says physicist Stephen Eubank.