Monogamous relationships are more likely to generate multiple strains of the same disease, rather than letting one win out, say British scientists who used mathematical models to investigate how human interactions influence epidemics.

Real human populations, the researchers say, can be divided into a sexually promiscuous core group, and a less active, monogamous group in which sexual contacts are fewer and sequential. These two subpopulations provide very different environments for a pathogen, said Ken Eames, a postdoc in biological sciences and mathematics at the University of Warwick and lead author of a study that appears in the August issue of The American Naturalist.

“Most mathematical models treat people as ping pong balls being batted about in a wind tunnel,” Eames said, “But if you think about how social interactions work, and particularly sexual partnerships, it all goes on for a lot longer than that.”

The model, which represents individuals as data points and interactions as lines between them, shows that when multiple strains of a disease are competing in the highly active group and are rapidly transmitted throughout the population, one strain will eventually dominate.

But in the monogamous group, the strains are passed slowly between individuals, and divide evenly in the population.

“If the partnership is really quick, the pathogen that is going to do best will spread itself really quickly onto the next person,” Eames said. “It doesn’t need to really hang around one individual that long.

“But when you don’t get all that many new partnerships, there’s a big advantage for the pathogen to last a long time in any individual,” he continued. “It’s going to have to wait a while until the individual bumps into someone else.”

Compared to other mathematical models scientists use to analyze the spread of disease, Eames said his attempts a more realistic representation by combining two factors: disease strain competition and the dynamics of human partnerships.

However, Martina Morris, a professor of sociology and statistics at the University of Washington, said there is no basis in the social sciences for Eames’s model, and that it doesn’t take advantage of any kind of empirically verified patterns of sexual behavior.

“It leaves me sort of scratching my head as to what the policy implications would be, since this doesn’t actually represent any known population,” she said.

Eames admits that it’s hard to say whether any real diseases fall into his group’s pattern.  Detailed work has yet to be done to examine properties of disease strains and the populations they infect, he said, though he would expect the model to be applicable to sexually transmitted diseases like chlamydia or gonorrhea.
“[The model] shows that human behavior doesn’t just change how much infection you’re going to get,” he said, “it also affects what type of infection it is.

“That’s going to have big impacts on public health systems, potentially.”

Originally published August 24, 2006


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