Researchers studied several populations in which HIV-AIDS are prevalent. (Image: Zimbabwean orphans, USAID)

Sometimes the most effective weapon against disease might not be a drug or vaccine, but a bit of well-turned mathematics. A small working group met at SFI in late February with the goal of improving the mathematical models used to understand, manage, and prevent infectious disease epidemics.

The participants at the meeting, titled “From Network Structure to Epidemiological Prediction,” sifted through data from eight independent epidemiological studies of HIV-AIDS in several human populations using various data- gathering methods.

Their aims in this initial meeting were to identify both the commonalities and differences among the data sets and infer the major factors behind the spread of infection through the study populations’ diverse social networks.

The project was made possible by a grant from SFI Trustee Bill Sick and his wife Stephanie. The meeting was organized by SFI External Professor Lauren Meyers, an associate professor of integrative biology at the University of Texas at Austin.

In attendance were Richard Rothenberg, a preventive medicine physician at Georgia State University’s Institute of Public Health, UC San Diego postdoctoral fellow Erik Volz, and private database consultant Stephen Muth.

The project team’s next step, says Lauren, will be to spend the next few months applying the insights they came up with toward generating better predictive models of disease-spread dynamics. By viewing diverse populations through the lens of network models, she says, we may discover more effective strategies for controlling outbreaks.

The group plans to meet one more time this summer to discuss how their results can be generalized to other populations and diseases and applied to real-world health practice. 

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