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by Elise Wachspress

Last week, the scientific journal eLife (established by the Howard Hughes Medical Institute, Max Planck Society, and the Wellcome Trust) published a remarkable study—a veritable “weather forecasting” tool to predict, in detail, the movement of the flu in the U.S.

Scientists from the University of Chicago, Microsoft Research, Columbia University, and Argonne National Laboratory used many gigantic, disparate datasets—insurance records of more than 150 million people, 1.7 billion geo-located Twitter posts, air traffic, weather, how often people visit friends nearby, vaccination rates, even mutations in the virus itself—over nine flu seasons, from 2003 to 2011, to create a model to describe in detail how flu epidemics start and spread.

The team then verified the accuracy of their computational model by “predicting” the spread of flu for three subsequent seasons (2014-2017), predictions which dovetailed with what actually happened, at resolutions higher than those created by the Centers for Disease Control and Prevention.

The scientists’ work documented that seasonal flu outbreaks generally originate in warm, humid areas of the south and southeast, mostly near the Gulf of Mexico or the Atlantic Ocean; though infections can originate in many places, they usually gain traction and grow into epidemics only near large bodies of water. Of the factors most important in spreading the virus beyond these coastal areas, foremost was the original host population’s “social connectedness”—how often people interacted on a day-to-day basis. The next most critical factors were weather (high humidity, high temperature, and low barometric pressure fostered the epidemic); changes in the virus itself; and land travel by the host population, which turned out to be much more powerful in fueling an epidemic than airplane travel.

The new model they derived offers a great tool to help public health officials improve prevention efforts, suggesting, perhaps, how best to deploy flu shots early on, and how people near the coasts should think about their local travel and socialization once people start getting sick. In fact, the model could be used to test prospective public health policies in silico before they are implemented, saving precious energy and resources.

But perhaps as interesting as the forecasting tool is the scientific “network” that created it.

The strength of weak ties

When you are looking for a job, identifying potential business investors, even finding a play group for your kids, it’s unlikely your spouse, siblings, or even immediate friends can help much. They know many of the same people and ideas you do. We’ve learned the power of networking, reaching out to those somewhat distant from us, for better answers. The internet and social media like Pinterest and YouTube have brought networking to a whole new level: they provide easy access to all kinds of far-flung, elusive information, from how to help your kids with their math homework or make crepe suzettes, to how to do your taxes or evaluate your investment portfolio.

Networks work most effectively when they include lots of weak, distant ties. The more people from disparate backgrounds you can reach even briefly or intermittently, the more likely you are to gain more knowledge and better ideas.

Andrey Rzhetsky, PhD, the leader of the flu model team, has mastered the art and science of networking.

autism network map

Rzhetsky’s network map of patterns of gene expression in brain tissue of people with autism, from a 2010 paper

He began his career as a mathematical biologist, analyzing gene sequences and connecting the dots, when computational biology was still in its infancy. By 1991 he was harnessing microcomputer programs to construct “phylogenetic trees”—categorizing differences among organisms—to better understand evolution. As more and more scientific research was published online, Rzhetsky became among the earliest developers of text mining, an automated way to search journal articles, capturing and cataloguing connections between genes, proteins, and diseases, without a human having to read each article.

He began training machines to suss out connections between all kinds of data. The “networks” of information he built ranged from molecular interactions in biological systems, to documenting how seemingly unrelated diseases overlap, to the reliability of scientific studies and much more, informing a growing variety of fields. What environmental factors might trigger autism or bipolar disorder? Which diseases inflict the greatest human suffering? How should scientists choose experiments to best accelerate the progress of science and medicine? Rzhetsky and his collaborators have mined and harnessed data to find useful answers to these disparate questions and more.

And the number and diversity of his collaborators on these projects is pretty stunning. Rzhetsky is ingenious at using weak ties to find expertise all over the world. Whether it is getting access to huge caches of medical records that stretch over generations (Denmark) or computational/artificial intelligence experts at major corporations (Seattle), Rzhetsky uses the power of weak ties to build bases of knowledge that push the boundaries of science. His cohort on the flu research provides an example:

  • Rzhetsky’s closest partner was Ishanu Chattopadhyay, PhD, now also a faculty member at UChicago. Chattopadhyay has developed algorithms for a broad swath of areas: path-planning for mobile robots, autonomous decision-making, swarming behavior and self-organization, and reverse-engineering measurements of populations, species, and gene expression to understand systems’ mechanisms.
  • Josh Elliott, PhD, started his career as a high-energy theoretical particle physicist. Over the past decade he’s applied those analytical skills to climate change, agricultural production, economic modeling, and the social sciences as a research scientist in the University of Chicago’s Energy Policy Institute.
  • Emre Kiciman, PhD, a principal researcher in artificial intelligence at Microsoft Research, lent his expertise in social media analysis, particularly attuned to its inherent systematic biases.
  • And Jeffrey Shaman, PhD, of Columbia University, provided proficiency in climate, atmospheric science, hydrology, and other environmental determinants of infectious disease transmission and forecast.

Scientific successes like theirs demonstrate that novel breakthroughs depend more and more on teams with diverse expertise, experiences, and viewpoints. Just as casting a wide net leads us to new recipes (and ways to avoid the flu!), so those on the forefront of discovery must reach across departments, institutions, even national boundaries to achieve the greatest knowledge.

Elise Wachspress is a senior communications strategist for the University of Chicago Medicine & Biological Sciences Development office