When Paired with Coinfection, Social Isolation Might Fuel Rather Than Foil Epidemics

In a recent paper published in the Proceedings of the National Academy of Sciences, Laurent Hébert-Dufresne and Benjamin Althouse show that when two separate diseases interact with each other, a population clustered into relatively isolated groups can ignite epidemics that spread like wildfire.

“We thought we understood how clustering works,” Hébert-Dufresne says, ”but it behaves exactly opposite to what we thought once interactions are added in. Our intuition was totally wrong.”

At the heart of the new study are two effects that have gained a lot of attention in recent years -- social clustering and coinfection -- but haven’t been studied together. That, Hébert-Dufresne and Althouse say, turns out to be a major omission.

Ordinarily, the pair says, clustering limits outbreaks. Maybe kids in one preschool get sick, for example, but because those kids don’t see kids from other preschools very often, they’re not likely to spread the disease very far.

Coinfection often works the other way. Once someone is sick with, say, pneumococcal pneumonia, they’re more likely than others to come down with the flu, lowering the bar for an epidemic of both diseases.

But put the effects together, the two discovered through computational modeling, and you get something that is more -- and different -- than the sum of its parts. While clustering works to prevent single-disease epidemics, interactions between diseases like pneumonia and the flu help keep each other going within a social group long enough that one of them can break out into other clusters, becoming a foothold for the other -- or perhaps a spark in a dry forest.

Once confection happens the diseases, Althouse says, “can catch fire.” The end result is a larger, more rapidly developing epidemic than would otherwise be possible.

That conclusion has immediate implications for public health officials, whose worst-case scenarios might be different or even tame compared with the outbreaks Hébert-Dufresne and Althouse hypothesize.

But there are interesting implications for network scientists and complex systems researchers, who often think in epidemiological terms. Two ideas, for example, might interact with one another such that both spread more rapidly than they would on their own, just as diseases do.

"We hope to take this work in new and different directions in epidemiology, social science, and the study of dynamic networks,” Althouse says. “There’s great potential.”

The paper appeared in the Proceedings of the National Academy of Sciences on July 20, 2015.