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Podcast

Episode 72: Understanding Epidemiologic and Algorithmic Models for COVID-19

May 19, 2020

Nilay Shah, PhD, chair of the division of health care policy and research, Mayo Clinic, explains the information and factors that go into the predictive models for the impact of COVID-19; examines the reliability of the Susceptible, Exposed, Infected, and Recovered (SEIR) model for policy decisions; breaks down some of the confounding effects of various input assumptions on the models; and more.

 

Dr Nabhan and Dr Shah have no relevant financial disclosures.

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