COVID-19 models are being used every day to predict the course and short- and long-term impacts of the pandemic. And we’ll be using these COVID-19 models for months to come. While many of us in healthcare are not epidemiologists or data scientists, we’re all sifting through the data to get a handle on how many people are going to get sick, how many will end up in the hospital or on a ventilator, and ultimately, how many people will die.
Government agencies are using models to set public policy, such as social distancing or shelter-in-place mandates, but confusion sets in because the various models often disagree. To understand the inherent disagreement in models, you must look at what goes into their development. Having this information will help you determine the best way to use and interpret predictive COVID-19 models.
Building a Model
For most of us, the process behind developing a model seems a little bit like the Wizard of Oz. It’s hard to pull back the curtain on the underlying details to understand how they work together to generate the ultimate output: predicting the future.
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Source: Hit Consultant