Real-world data, not predictions, should drive decisions on Covid school opening
By , , and
April 23, 2021
The United States has exceeded 31 million Covid-19 infections (a messy data point) and is approaching 570,000 Covid-19 deaths (a more robust data point). Yet despite the abundance of data about the pandemic, the best available information is not usually what guides policymakers.
Some policies are senselessly cruel, such as keeping family members from visiting loved ones dying of Covid-19. Others heighten disparities in income, health, and education.
After nine months of observing school closures and reopenings, we identified two factors that appear to be influencing decision-makers toward making less rational, less effective school-reopening policies: overreliance on alarming “predictive” models that are not actually predictive, and media reports based on data that are poorly analyzed and then manipulated to fit preconceived negative narratives. We propose three simple solutions to address these factors.
Don’t use doomsday scenarios based on flawed models for planning purposes
Models that later turn out to be inaccurate have distorted Covid-19 policies since the pandemic started. Such models have been cited repeatedly by school administrators, legislators, and governors as reasons schools should close, or remain closed.
In January 2021, for example, a biostatistician modeler serving as a consultant to a large school district in Oregon gave a presentation to a widely attended public school board meeting. His model predicted a large local spike in Covid-19 hospitalizations in February and March, based on assumptions about variants and mitigation fatigue. This model showed hospital cases in Oregon nearly doubling between January and March.
In a video of the meeting, school board members can be seen asking fearful questions about this grim scenario. One board member comments, “My fear is we are going to get both the fatigue and the variant.”
Yet between January and March, hospitalizations in the region actually fell by 66%.
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