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Understanding Epidemiologic and Algorithmic Models for COVID-19
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.
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By By Chadi Nabhan
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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.