Scientists used social-network analysis to find the origins of an outbreak of tuberculosis (top).
It was the baby’s case that first caught people’s attention: an infant in a medium-sized community in British Columbia that was diagnosed with tuberculosis in July 2006. When public health workers took a deeper look at the community’s medical records, they found a number of additional cases suggestive of an outbreak. By December 2008, 41 cases had been identified, bumping up the region’s annual incidence rate by a factor of 10.
Officials at the BC Centre for Disease Control (BCCDC) were faced with the question at the heart of any outbreak: what was the source? Had the bacteria that cause TB mutated to become more infectious? Or was there some change in the community that made the microbes more likely to spread?
The answer would be crucial in focusing public health efforts to stop it. Traditional methods for analyzing transmission patterns created only a hazy picture. Molecular analysis of specimens collected from patients suggested everyone was infected with the same strain. “Based on the information we had, we couldn’t really figure out who was giving it to whom,” says Patrick Tang, a medical microbiologist at the BCCDC.
So Tang and collaborators combined two tools to create a much clearer picture of the outbreak: social-network analysis, which has become increasingly common in tracking infectious disease over the last decade, and whole-genome sequencing—an analysis of the microbe’s entire DNA sequence. The latter, which has been applied to outbreaks in only a few cases to date, allows much more precise tracing of infections than traditional molecular techniques, which look at only a few spots in the genome.
“For the first time, we can paint a really detailed picture of the relationships between people in the community and a really detailed picture of the relationships between the bacteria themselves,” says Jennifer Gardy, head of the BCCDC’s Genome Research Laboratory and lead author on the study. “We can reconstruct the path an organism took throughout a population.”
Researchers sequenced the genomes of 36 bacterial samples collected from patients. They used specialized algorithms to compare individual mutations that arose in the microbes’ DNA as they spread. The analysis, published today in the New England Journal of Medicine, revealed that there were actually two different lineages of the microbe, pointing to two different outbreaks spreading independently of one another. These findings suggested that an environmental factor lay at the heart of the outbreak, rather than a genetic one.
In addition to genome sequencing, researchers questioned patients about the people they lived with and worked with, as well as where they spent their time, creating a network diagram of potential interactions. “Instead of just getting a list of names, you’re getting names, places, and behaviors, and you can paint a much more detailed picture of the underlying community structure,” says Gardy. “Key people and places and certain behaviors that might be contributing to an outbreak’s spread become much more apparent, and allow you to adjust your outbreak investigation in real-time as this new information becomes available.”
The researchers could overlay the genetic data identifying individual mutations with information from the social network that pinpointed when different people might have interacted with each other. “We could identify super-spreaders of the disease,” says Tang.
The researchers ultimately concluded that the outbreak was linked to an increase in crack cocaine use in the community. “That was the most likely trigger, reactivating latent disease and facilitating the spread of disease,” says Tang. Using this information, public health agencies could focus their resources on the root of the problem and identify those at the highest risk for reactivation of TB, he says.
“The findings show that it is feasible to combine genetic data and social structure to give an idea of the transmission chain and to distinguish two outbreaks going on at the same time,” says Joel Miller, a research fellow in the Center for Communicable Disease Dynamics at the Harvard School of Public Health.
Tang and others predict that this approach will become commonplace in the next few years. “With the cost of whole-genome sequencing coming down—it’s a few hundred dollars per organism—a lot of people are interested in using it to address questions worldwide,” says Tang. He says sequencing will be especially important in more complex cases, such as tracking the spread of antibiotic-resistant organisms around the world.
The main hurdle now is not the cost of sequencing, but rather the analysis tools, adds Tang. “The limitation for most people is how to make sense of the genomic data that is generated,” he says.