In an editorial accompanying the study in the journal, Angela McLean, a professor of mathematical biology at Oxford University in England, wrote that the new model has advantages over previous ways of predicting disease spread.
"Faced with the complexity of the global spread of new infections, a common approach has been to create enormous computer simulations," McLean wrote.
But such sophisticated approaches have yielded little insight, she said.
Disease-spread models that are based on a germ's mobility, epidemiological data and disease-specific mechanisms are also difficult to implement and have limited use if such factors are unknown, which is typically the case with a newly emerging infectious diseases, the researchers said.
In contrast, the new model could predict arrival times for a disease, even if little is known about the microbe that causes it, the researchers said.
"Given the projected growth of passenger numbers over the coming decades, this theory may be able to illuminate how much faster the next SARS or H1N1 will spread as more and more people take to the sky," McLean said.
More From LiveScience:
Copyright 2013 LiveScience, a TechMediaNetwork company. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.
This story originally appeared on LiveScience.com.