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Predicting intervention strategies for swine flu

Predicting intervention strategies for swine flu

lauren ancel meyersLauren Ancel Meyers, associate professor of integrative biology, is using the Texas Advanced Computng Center’s (TACC) Lonestar supercomputer to predict how the new strain of H1N1 flu is spreading throughout North America and to determine the best intervention strategies.

“Our goal is to develop powerful and flexible software so that public health agencies like the Center for Disease Control (CDC) and British Columbia CDC can come to us with candidate intervention strategies and we can use our model to predict their effectiveness, and improve them,” said Meyers, associate director of the Division of Statistics and Scientific Computation.

For example, the United States has roughly 80 million doses of anti-viral drugs in a stockpile. The important public health questions are: Given that a stockpile of anti-virals exists, which communities should get the first limited doses and in what quantity? What cities are the targets and what is the best timing for the other releases? Should the CDC release the drugs to cities in proportion to population size? Would it be better to target releases to areas where swine flu cases already exist?

Meyers and her team are addressing these questions with their models. They have incorporated the latest information about H1N1 flu and are using their optimization software to determine the best timing and targets for vaccine, antiviral and other intervention resources. At this point, the results are for demonstration purposes only, but they illustrate the potential utility of these important computational tools.

According to Meyers, infectious disease epidemiology, especially on the modeling side, has advanced tremendously in the last decade.

“In the past, we’ve taken methods from statistical physics for modeling the spread of a liquid through a network and applied them to modeling the spread of disease through contact patterns in human populations,” Meyers said. “In this case, we’re taking methods developed for operations research and applying them to epidemiology to optimize intervention strategies. Multi-disciplinary research is fueling great advances in our ability to predict and control infectious disease like North American H1N1 flu.”

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This originally appeared as part of this story on the TACC website.

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Tuesday, 01 December 2020

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