As the avian influenza A(H7N9) continues to strike in China this year, it has begun to carry a mutation that makes it immune to common flu drugs. Though H7N9 is difficult to transmit between humans, there is still a concern that it might become a pandemic — and some scientists hope they can forecast such outbreaks.

Scientists at Northwestern University have developed a new mathematical theory that shows how disease spreads globally by using air transportation data. The researchers hope they will be able to help forecast a disease outbreak, as well as understand its origin and global pathways, as it spreads.

“With this new theory, we can reconstruct outbreak origins with higher confidence, compute epidemic-spreading speed and forecast when an epidemic wave front is to arrive at any location worldwide,” theoretical physicist Dirk Brockmann said in a news release. Brockmann developed the research ideas at the Northwestern Institute on Complex Systems (NICO), and is currently a professor at Humboldt-University in Berlin, where he worked with scientist Dirk Helbing on the project. Helbing is a professor at ETH Zurich.

The study “The Hidden Geometry of Complex, Network-Driven Contagion Phenomena,” which will be published Friday in Science, shows how the new mathematical theory defines an understanding of the global spread of epidemics. For example, they’ll be able to analyze the answers to several questions when a new disease sprouts up in various parts of the world: Where are new cases to be expected? Where did this disease begin? When are new cases expected, and how many people have a risk of being infected?

In the study, the authors focused on “effective” distance, which is how two locations are connected by a strong link, and can therefore be considered closer than a location that is “geographically” near. Effective distance cannot be necessarily measured in miles, but rather in economic distance such as transportation cost, communication distance, or social distance. “In the context of global, air-traffic-mediated epidemics, we show that effective distance reliably predicts disease arrival times,” the authors write in their abstract. “Even if epidemiological parameters are unknown, the method can still deliver relative arrival times.”

The Models of Infectious Disease Agent Study (MIDAS), which has been around for years, works on projects in a similar vein of research. MIDAS was founded by the National Institutes of Health to study the spread of infectious diseases using computational, statistical, and mathematical models. It has analyzed the spread of flus, pertussis, West Nile disease, cholera and dengue fever. This year, MIDAS has been working on designing FRED (A Framework for Reconstructing Epidemiological Dynamics), a software program that can create virtual outbreaks and bring them to a smartphone, which could help public health workers to use modeling tools away from their computers.

“In the future, we hope our approach can substantially improve existing, state-of-the-art models for disease spread,” Brockmann said in the news release.

 

Source: Brockmann D, Helbing D. “The Hidden Geometry of Complex, Network-Driven Contagion Phenomena.” Science. 2013.