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The Wall Street Journal interviewed UCLA Fielding School of Public Health dean and professor of biostatistics Ron Brookmeyer on the mathematics and modeling behind predictions about the pandemic, including the SEIR method.
The near doubling of coronavirus death predictions in a closely followed model this week underscores a frustrating reality for officials weighing how and when to reopen society: Many basic facts about the new coronavirus remain unknown.
Epidemiologists have created many computer models to predict surge capacity in the health-care system and guide policy-making. These seek to predict how many people might be infected, how many will die, and when and how transmission might slow or speed up.
But the models are only as good as the underlying data and knowledge about the disease. Models are based on assumptions and estimates and shift with new information, often because of our own changing behavior but also because the scientific understanding of this newly emerged virus is still evolving. Researchers have strained to pin down basic bits of information about the disease, such as its infectiousness. Undercounted infections and deaths have blinded public-health authorities and modelers alike to the full scope of the pandemic.