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Models Are Only as Good as the Assumptions Built Into Them (Guest: Kevin Dayaratna)

May 11, 2020

The results of epidemiological models projecting coronavirus infection and mortality rates depend upon the assumptions built into them. Learn how the models work.

The results of epidemiological models projecting coronavirus infection and mortality rates are highly sensitive to the assumptions built into them. Only be staying on top of new data or evidence, and questioning assumptions will we build more accurate forecasts for the coronavirus and future pandemics. The same is true of climate models, with the added confounding factor that most of them are shaped by political considerations, the desire for power and control, not just simply driven by a quest for understanding the truth.

Author
H. Sterling Burnett, Ph.D. is a Heartland senior fellow on environmental policy and the managing editor of Environment & Climate News.
hsburnett@heartland.org
Author
Kevin D. Dayaratna is Senior Statistician and Research Programmer in The Heritage Foundation’s Center for Data Analysis (CDA) and a Policy Advisor with the Heartland Institute.