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[Slashdot] - 'Why Modeling the Spread of COVID-19 Is So Damn Hard'

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Slashdot reader the_newsbeagle writes: At the beginning of the pandemic, modelers pulled out everything they had to predict the spread of the virus. This article explains the three main types of models used: 1) compartmental models that sort people into categories of exposure and recovery, 2) data-driven models that often use neural networks to make predictions, and 3) agent-based models that are something like a Sim Pandemic. "Researchers say they've learned a lot of lessons modeling this pandemic, lessons that will carry over to the next..." the article points out: Finally, researchers emphasize the need for agility. Jarad Niemi, an associate professor of statistics at Iowa State University who helps run the forecast hub used by the CDC, says software packages have made it easier to build models quickly, and the code-sharing site GitHub lets people share and compare their models. COVID-19 is giving modelers a chance to try out all their newest tools, says biologist Lauren Ancel Meyers, the head of the COVID-19 Modeling Consortium at the University of Texas at Austin. "The pace of innovation, the pace of development, is unlike ever before," she says. "There are new statistical methods, new kinds of data, new model structures." "If we want to beat this virus," says Mikhail Prokopenko, a computer scientist at the University of Sydney, "we have to be as adaptive as it is."

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