A new mathematical model from Britain promises to help scientists unravel and predict how STDs spread, researchers reported Monday. "[STDs] are often overlooked... but they are increasing in prevalence all over the world," said Matt Keeling, senior mathematical epidemiologist at the University of Warwick. There are an estimated 3 million cases of chlamydia annually in the United States, Keeling noted. The American Social Health Association reports one in five people in the United States has an STD, and two-thirds of the cases occur in people age 25 or younger.

Past models of STD dynamics were either too simple, by assuming indiscriminate pairing of infected and susceptible individuals, or required far more information than was realistically feasible to collect.

"When it comes to classical airborne diseases like influenza, it's very obvious who you can transmit it to -- you pass it to someone while on the train or in the shop. But when it comes to [STDs], they don't hop around in a random mixing model," said Keeling.

Keeling and colleague Ken Eames developed a relatively simple new model that takes sexual partnerships into account while not worrying about other traits. Even if only limited information is available on sexual partnerships -- the kind of data clinics often gather, for instance -- Keeling said the new model proved accurate, agreeing "very well" with the findings from both more complex models and real world examples.

The new model consists of a set of equations. By plugging a few numbers into the equations, such as the size of the population or the rate at which a given STD spreads, the model estimates what percentage of a population is infected over time. The researchers noted the bare bones model is not supposed to be perfect, given its simplicity. However, with this model as a solid basis, Keeling feels medical investigators can easily create systems to help predict the course of specific epidemics. "If we wanted to model chlamydia in New York, we could probably put one together in a couple of weeks," Keeling said.

The innate simplicity of the model should allow scientists to figure out how best to target infection treatment and control programs with limited health care resources. Eames and Keeling reported their results in the online edition of the Proceedings of the National Academy of Sciences.

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