Communications of the ACM
Scientists Develop Tool to Improve Disease Model Accuracy
A new mathematical tool could help scientists to deliver more accurate predictions of how diseases, including COVID-19, spread through towns and cities around the world.
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Researchers at the University of Colorado Boulder (CU Boulder) and Rio de Janeiro State University in Brazil have developed an embedded discrepancy operator, a new tool that could help scientists to deliver more accurate predictions of how diseases, such as COVID-19, spread through populations around the world.
The researchers used the 2016 Zika virus outbreak as a test case, and found that the embedded discrepancy operator could be used to help scientists fix models that fall short of their goals, effectively aligning model results with real-world data.
When researchers feed data into the embedded discrepancy operator, it sees and responds to the information, then rewrites the model's underlying equations to better match reality.
While the researchers concede their findings are specific to Zika, they say they will try to adapt their methods to help scientists deal with the current COVID-19 pandemic.
From University of Colorado Boulder
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Abstracts Copyright © 2020 SmithBucklin, Washington, DC, USA
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